@Article{info:doi/10.2196/60332, author="Egan, L. Kathleen and Cox, J. Melissa and Helme, W. Donald and Jackson, Todd J. and Richman, R. Alice", title="Text Message Intervention to Facilitate Secure Storage and Disposal of Prescription Opioids to Prevent Diversion and Misuse: Protocol for a Randomized Controlled Trial", journal="JMIR Res Protoc", year="2025", month="Apr", day="17", volume="14", pages="e60332", keywords="prescription opioid", keywords="storage", keywords="disposal", keywords="text message intervention", keywords="randomized controlled trial", keywords="mobile phone", abstract="Background: Nonmedical use of prescription opioids remains a critical public health issue; 8.5 million people in the United States misused opioids in 2022. Most people obtain prescription opioids for misuse from family or friends. Thus, facilitating secure storage and disposal of opioid medications during and after treatment is needed to prevent medication diversion and subsequent misuse. Objective: The primary objective of this study is to test the feasibility and efficacy of a novel intervention that uses a persuasive, informational SMS text message reminder system to enhance the impact of secure storage and disposal of unused opioid medications. We hypothesize that the SMS text message intervention will increase secure storage during treatment and disposal of prescription opioids after treatment. Methods: We will use a 2-arm randomized controlled trial to test the intervention for feasibility and efficacy. Participants (aged 18+ years who have received an opioid prescription in the past 2 weeks) will be randomly assigned to either receive the SMS text message intervention or standard-of-care educational materials. Participants in the intervention will receive 4 SMS text messages related to secure storage and 3 messages related to disposal. All participants will complete baseline, midpoint (day 25), and postintervention (day 45) evaluation surveys. We will test whether receipt of the intervention is associated with two primary outcomes, which are (1) secure storage of prescription opioid medication (locked vs unlocked) and (2) disposal of unused prescription opioid medication (disposed vs not disposed). We will use multiple logistic regression to test the main hypotheses that the intervention will be positively associated with secure storage (locked vs unlocked) and disposal (yes vs no) behaviors, which will allow us to control for demographic variables known to influence the outcomes. This protocol represents the entire structure of the randomized controlled trial. Results: Recruitment for the randomized controlled trial was launched in April 2024, and data collection was completed in December 2024. The final sample size is 484. Data analyses for the main hypothesis will be completed by May 2025, and the main hypothesis manuscript will be submitted for publication by May 2025. Conclusions: Results from this study will indicate whether a text message reminder system can increase secure storage and disposal behaviors for individuals who receive opioid medication. This type of intervention has the potential to be integrated into currently used health care delivery systems, such as prescription pickup reminders at pharmacies. Thus, the intervention is scalable across systems of care, thus expanding the reach of secure storage and disposal programs to prevent prescription opioid misuse. Trial Registration: ClinicalTrials.gov NCT05503186; https://clinicaltrials.gov/study/NCT05503186 International Registered Report Identifier (IRRID): DERR1-10.2196/60332 ", doi="10.2196/60332", url="https://www.researchprotocols.org/2025/1/e60332" } @Article{info:doi/10.2196/59767, author="Yeung, Kan Andy Wai and Hammerle, Peter Fabian and Behrens, Sybille and Matin, Maima and Mickael, Michel-Edwar and Litvinova, Olena and Parvanov, D. Emil and Kletecka-Pulker, Maria and Atanasov, G. Atanas", title="Online Information About Side Effects and Safety Concerns of Semaglutide: Mixed Methods Study of YouTube Videos", journal="JMIR Infodemiology", year="2025", month="Apr", day="8", volume="5", pages="e59767", keywords="YouTube", keywords="semaglutide", keywords="social media", keywords="Ozempic", keywords="Wegovy", keywords="Rybelsus", keywords="safety", keywords="knowledge exchange", keywords="side effects", keywords="online information", keywords="online", keywords="videos", keywords="health issues", keywords="drugs", keywords="weight loss", keywords="assessment", keywords="long-term data", keywords="consultation", abstract="Background: Social media has been extensively used by the public to seek information and share views on health issues. Recently, the proper and off-label use of semaglutide drugs for weight loss has attracted huge media attention and led to temporary supply shortages. Objective: The aim of this study was to perform a content analysis on English YouTube (Google) videos related to semaglutide. Methods: YouTube was searched with the words semaglutide, Ozempic, Wegovy, and Rybelsus. The first 30 full-length videos (videos without a time limit) and 30 shorts (videos that are no longer than 1 minute) resulting from each search word were recorded. After discounting duplicates resulting from multiple searches, a total of 96 full-length videos and 93 shorts were analyzed. Video content was evaluated by 3 tools, that is, a custom checklist, a Global Quality Score (GQS), and Modified DISCERN. Readability and sentiment of the transcripts were also assessed. Results: There was no significant difference in the mean number of views between full-length videos and shorts (mean 288,563.1, SD 513,598.3 vs mean 188,465.2, SD 780,376.2, P=.30). The former had better content quality in terms of GQS, Modified DISCERN, and the number of mentioned points from the custom checklist (all P<.001). The transcript readability of both types of videos was at a fairly easy level and mainly had a neutral tone. Full-length videos from health sources had a higher content quality in terms of GQS and Modified DISCERN (both P<.001) than their counterparts. Conclusions: The analyzed videos lacked coverage of several important aspects, including the lack of long-term data, the persistence of side effects due to the long half-life of semaglutide, and the risk of counterfeit drugs. It is crucial for the public to be aware that videos cannot replace consultations with physicians. ", doi="10.2196/59767", url="https://infodemiology.jmir.org/2025/1/e59767" } @Article{info:doi/10.2196/63683, author="Cagnacci, Angelo and Grandi, Giovanni and Capobianco, Giampiero and Fulghesu, Maria Anna and Morgante, Giuseppe and Biondelli, Vincenzo and Piccolo, Elena and Casolati, Elena and Mangrella, Mario", title="Effects of a Monophasic Hormonal Contraceptive With Norgestimate+Ethinyl Estradiol on Menstrual Bleeding: Protocol and Design of a Multicenter, Prospective, Open-Label, Noncomparative Study in Italy", journal="JMIR Res Protoc", year="2025", month="Mar", day="31", volume="14", pages="e63683", keywords="combined oral contraceptive", keywords="ethinyl estradiol", keywords="menstrual cycle", keywords="monophasic", keywords="norgestimate", keywords="hormonal contraceptive", keywords="menstrual health", keywords="Italy", keywords="women's health", keywords="patient-reported outcomes", keywords="methodology", keywords="observational study", keywords="reproductive health", keywords="data analysis", keywords="assessment", abstract="Background: Norgestimate (NGM) is a progestin with negligible androgenic activity that is available in combination with ethinyl estradiol (EE) as a monophasic combined oral contraceptive (COC). It has been more than 30 years since a clinical study evaluated the effects of monophasic NGM/EE on menstrual cycle characteristics in healthy women, and in the interim, there has been growing recognition that clinical trials of contraceptives should evaluate a wide range of potential positive and negative impacts for users. Objective: The aim of this study is to investigate menstrual cycle control during the use of a monophasic COC formulation containing NGM 0.25 mg and EE 0.035 mg (Effimia; Italfarmaco SpA), using established methodologies as well as patient-reported outcomes. Methods: This is a prospective observational study being undertaken in a target population of 228 healthy Italian women aged 18-35 years who are starting oral contraception for the first time or switching from another COC. The participants are asked to complete a diary for 6 cycles recording information about their menstrual cycles (frequency, duration, regularity, estimated flow volume, and breakthrough bleeding), any unscheduled bleeding, and an evaluation of dysmenorrhea, using a 100-mm visual analog scale from 0=no pain to 100=very severe pain, and any adverse events. Compliance is assessed after 3 and 6 months via returned medication. The primary end point is the change from baseline in the rate of intermenstrual bleeding during the sixth cycle. At baseline, 3 months, and 6 months, acne will also be assessed using the Global Acne Grading Scale, and participants will complete a Profile of Mood State to assess premenstrual syndrome and the Female Sexual Function Index to evaluate the quality of their sex life. A subgroup of 28 participants at 1 site (Genoa) is also providing a blood sample for the assessment of metabolic, endocrine, and coagulation parameters. Results: Study enrollment began in July 2023 and is expected to be complete by December 2024. Data analysis is expected to be complete by October 2025. Conclusions: This study into the effects of monophasic NGM/EE 0.25/0.035 mg on menstrual characteristics in healthy Italian women will provide up-to-date data on these effects and includes assessments of a range of other parameters, such as acne severity and patient-reported outcomes, in line with recent international consensus recommendations. Trial Registration: ClinicalTrials.gov NCT06067256; https://clinicaltrials.gov/study/NCT06067256 and EudraCT 2021-003027-15; https://www.clinicaltrialsregister.eu/ctr-search/trial/2021-003027-15/IT International Registered Report Identifier (IRRID): DERR1-10.2196/63683 ", doi="10.2196/63683", url="https://www.researchprotocols.org/2025/1/e63683" } @Article{info:doi/10.2196/63983, author="Lee, Heonyi and Kim, Yi-Jun and Kim, Jin-Hong and Kim, Soo-Kyung and Jeong, Tae-Dong", title="Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study", journal="J Med Internet Res", year="2025", month="Mar", day="31", volume="27", pages="e63983", keywords="algorithm", keywords="machine learning", keywords="therapeutic drug monitoring", keywords="vancomycin", keywords="area under curve", keywords="pharmacokinetics", keywords="vancomycin dosing", abstract="Background: Vancomycin is commonly dosed using standard weight--based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive model that personalizes initial dosing based on patient-specific pharmacokinetic factors prior to administration may enhance target attainment and minimize the need for subsequent dose adjustments. Objective: This study aimed to develop and evaluate a machine learning (ML)--based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under the curve to minimum inhibitory concentration, thereby optimizing the initial vancomycin dosage. Methods: A retrospective cohort study was conducted using hospitalized patients who received intravenous vancomycin and underwent pharmacokinetic TDM consultation (n=415). The cohort was randomly divided into training and testing datasets in a 7:3 ratio, and multiple ML techniques were used to develop an algorithm for optimizing initial vancomycin dosing. The optimal algorithm, referred to as the OPTIVAN algorithm, was selected and validated using an external cohort (n=268). We evaluated the performance of 4 ML models: gradient boosting machine, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB). Additionally, a web-based clinical support tool was developed to facilitate real-time vancomycin TDM application in clinical practice. Results: The SVM algorithm demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.832 (95\% CI 0.753-0.900) for the training dataset and 0.720 (95\% CI 0.654-0.783) for the external validation dataset. The gradient boosting machine followed closely with AUROC scores of 0.802 (95\% CI 0.667-0.857) for the training dataset and 0.689 (95\% CI 0.596-0.733) for the validation dataset. In contrast, both XGB and RF exhibited relatively lower performance. XGB achieved AUROC values of 0.769 (95\% CI 0.671-0.853) for the training set and 0.707 (95\% CI 0.644-0.772) for the validation set, while RF recorded AUROC scores of 0.759 (95\% CI 0.656-0.846) for the test dataset and 0.693 (95\% CI 0.625-0.757) for the external validation set. The SVM model incorporated 7 covariates: age, BMI, glucose, blood urea nitrogen, estimated glomerular filtration rate, hematocrit, and daily dose per body weight. Subgroup analyses demonstrated consistent performance across different patient categories, such as renal function, sex, and BMI. A web-based TDM analysis tool was developed using the OPTIVAN algorithm. Conclusions: The OPTIVAN algorithm represents a significant advancement in personalized initial vancomycin dosing, addressing the limitations of current TDM practices. By optimizing the initial dose, this algorithm may reduce the need for subsequent dosage adjustments. The algorithm's web-based app is easy to use, making it a practical tool for clinicians. This study highlights the potential of ML to enhance the effectiveness of vancomycin treatment. ", doi="10.2196/63983", url="https://www.jmir.org/2025/1/e63983" } @Article{info:doi/10.2196/67361, author="Amoozegar, B. Jacqueline and Williams, Peyton and Giombi, C. Kristen and Richardson, Courtney and Shenkar, Ella and Watkins, L. Rebecca and O'Donoghue, C. Amie and Sullivan, W. Helen", title="Consumer Engagement With Risk Information on Prescription Drug Social Media Pages: Findings From In-Depth Interviews", journal="J Med Internet Res", year="2025", month="Mar", day="25", volume="27", pages="e67361", keywords="social media", keywords="prescription drugs", keywords="risk information", keywords="safety information", keywords="Facebook", keywords="Instagram", keywords="prescription", keywords="risk", keywords="information", keywords="safety", keywords="interview", keywords="consumer engagement", keywords="digital", keywords="drug promotion", keywords="user experience", keywords="promotion", abstract="Background: The volume of digital drug promotion has grown over time, and social media has become a source of information about prescription drugs for many consumers. Pharmaceutical companies currently present risk information about prescription drugs they promote in a variety of ways within and across social media platforms. There is scarce research on consumers' interactions with prescription drug promotion on social media, particularly on which features may facilitate or inhibit consumers' ability to find, review, and comprehend drug information. This is concerning because it is critical for consumers to know and weigh drug benefits and risks to be able to make informed decisions regarding medical treatment. Objective: We aimed to develop an understanding of the user interface (UI) and user experience (UX) of social media pages and posts created by pharmaceutical companies to promote drugs and how UI or UX design features impact consumers' interactions with drug information. Methods: We conducted in-person interviews with 54 consumers segmented into groups by device type (laptop or mobile phone), social media platform (Facebook or Instagram), and age. Interviewers asked participants to navigate to and review a series of 4 pages and 3 posts on their assigned device and platform. Interviewers encouraged participants to ``think aloud,'' as they interacted with the stimuli during a brief observation period. Following each observation period, participants were asked probing questions. An analyst reviewed video recordings of the observation periods to abstract quantitative interaction data on whether a participant clicked on or viewed risk information at each location it appeared on each page. Participants' responses were organized in a metamatrix, which we used to conduct thematic analysis. Results: Observational data revealed that 59\% of participants using Facebook and 70\% of participants using Instagram viewed risk information in at least 1 possible location on average across all pages tested during the observation period. There was not a single location across the Facebook pages that participants commonly clicked on to view risk information. However, a video with scrolling risk information attracted more views than other features. On Instagram, at least half of the participants consistently clicked on the highlighted story with risk information across the pages. Although thematic analysis showed that most participants were able to identify the official pages and risk information for each drug, auto-scrolling text and text size posed barriers to identification and comprehensive review for some participants. Participants generally found it more difficult to identify the drugs' indications than risks. Participants using Instagram more frequently reported challenges identifying risks and indications compared to those using Facebook. Conclusions: UI or UX design features can facilitate or pose barriers to users' identification, review, and comprehension of the risk information provided on prescription drugs' social media pages and posts. ", doi="10.2196/67361", url="https://www.jmir.org/2025/1/e67361" } @Article{info:doi/10.2196/57697, author="Haegens, L. Lex and Huiskes, B. Victor J. and van den Bemt, F. Bart J. and Bekker, L. Charlotte", title="Factors Influencing the Intentions of Patients With Inflammatory Rheumatic Diseases to Use a Digital Human for Medication Information: Qualitative Study", journal="J Med Internet Res", year="2025", month="Mar", day="13", volume="27", pages="e57697", keywords="digital human", keywords="information provision", keywords="intention to use", keywords="qualitative study", keywords="focus groups", keywords="drug-related problems", keywords="medication safety", keywords="safety information", keywords="information seeking", keywords="Netherlands", keywords="Pharmacotherapy", keywords="medication", keywords="telehealth", keywords="communication technologies", keywords="medication information", keywords="rheumatic diseases", keywords="rheumatology", abstract="Background: Introduction: Patients with inflammatory rheumatic diseases (IRDs) frequently experience drug-related problems (DRPs). DRPs can have negative health consequences and should be addressed promptly to prevent complications. A digital human, which is an embodied conversational agent, could provide medication-related information in a time- and place-independent manner to support patients in preventing and decreasing DRPs. Objective: This study aims to identify factors that influence the intention of patients with IRDs to use a digital human to retrieve medication-related information. Methods: A qualitative study with 3 in-person focus groups was conducted among adult patients diagnosed with an IRD in the Netherlands. The prototype of a digital human is an innovative tool that provides spoken answers to medication-related questions and provides information linked to the topic, such as (instructional) videos, drug leaflets, and other relevant sources. Before the focus group, participants completed a preparatory exercise at home to become familiar with the digital human. A semistructured interview guide based on the Proctor framework for implementation determinants was used to interview participants about the acceptability, adoption, appropriateness, costs, feasibility, fidelity, penetration, and sustainability of the digital human. Focus groups were recorded, transcribed, and analyzed thematically. Results: The participants included 22 patients, with a median age of 68 (IQR 52-75) years, of whom 64\% (n=22) were female. In total, 6 themes describing factors influencing patients' intention to use a digital human were identified: (1) the degree to which individual needs for medication-related information are met; (2) confidence in one's ability to use the digital human; (3) the degree to which using the digital human resembles interacting with a human; (4) technical functioning of the digital human; (5) privacy and security; and (6) expected benefit of using the digital human. Conclusions: The intention of patients with IRDs to use a novel digital human to retrieve medication-related information was influenced by factors related to each patient's information needs and confidence in their ability to use the digital human, features of the digital human, and the expected benefits of using the digital human. These identified themes should be considered during the further development of the digital human and during implementation to increase intention to use and future adoption. Thereafter, the effect of applying a digital human as an instrument to improve patients' self-management regarding DRPs could be researched. ", doi="10.2196/57697", url="https://www.jmir.org/2025/1/e57697" } @Article{info:doi/10.2196/65651, author="Bena{\"i}che, Alexandre and Billaut-Laden, Ingrid and Randriamihaja, Herivelo and Bertocchio, Jean-Philippe", title="Assessment of the Efficiency of a ChatGPT-Based Tool, MyGenAssist, in an Industry Pharmacovigilance Department for Case Documentation: Cross-Over Study", journal="J Med Internet Res", year="2025", month="Mar", day="10", volume="27", pages="e65651", keywords="MyGenAssist", keywords="large language model", keywords="artificial intelligence", keywords="ChatGPT", keywords="pharmacovigilance", keywords="efficiency", abstract="Background: At the end of 2023, Bayer AG launched its own internal large language model (LLM), MyGenAssist, based on ChatGPT technology to overcome data privacy concerns. It may offer the possibility to decrease their harshness and save time spent on repetitive and recurrent tasks that could then be dedicated to activities with higher added value. Although there is a current worldwide reflection on whether artificial intelligence should be integrated into pharmacovigilance, medical literature does not provide enough data concerning LLMs and their daily applications in such a setting. Here, we studied how this tool could improve the case documentation process, which is a duty for authorization holders as per European and French good vigilance practices. Objective: The aim of the study is to test whether the use of an LLM could improve the pharmacovigilance documentation process. Methods: MyGenAssist was trained to draft templates for case documentation letters meant to be sent to the reporters. Information provided within the template changes depending on the case: such data come from a table sent to the LLM. We then measured the time spent on each case for a period of 4 months (2 months before using the tool and 2 months after its implementation). A multiple linear regression model was created with the time spent on each case as the explained variable, and all parameters that could influence this time were included as explanatory variables (use of MyGenAssist, type of recipient, number of questions, and user). To test if the use of this tool impacts the process, we compared the recipients' response rates with and without the use of MyGenAssist. Results: An average of 23.3\% (95\% CI 13.8\%-32.8\%) of time saving was made thanks to MyGenAssist (P<.001; adjusted R2=0.286) on each case, which could represent an average of 10.7 (SD 3.6) working days saved each year. The answer rate was not modified by the use of MyGenAssist (20/48, 42\% vs 27/74, 36\%; P=.57) whether the recipient was a physician or a patient. No significant difference was found regarding the time spent by the recipient to answer (mean 2.20, SD 3.27 days vs mean 2.65, SD 3.30 days after the last attempt of contact; P=.64). The implementation of MyGenAssist for this activity only required a 2-hour training session for the pharmacovigilance team. Conclusions: Our study is the first to show that a ChatGPT-based tool can improve the efficiency of a good practice activity without needing a long training session for the affected workforce. These first encouraging results could be an incentive for the implementation of LLMs in other processes. ", doi="10.2196/65651", url="https://www.jmir.org/2025/1/e65651" } @Article{info:doi/10.2196/63755, author="Li, Wanxin and Hua, Yining and Zhou, Peilin and Zhou, Li and Xu, Xin and Yang, Jie", title="Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis", journal="J Med Internet Res", year="2025", month="Mar", day="5", volume="27", pages="e63755", keywords="COVID-19", keywords="natural language processing", keywords="drugs", keywords="social media", keywords="pharmacovigilance", keywords="public health", abstract="Background: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collection times. Objective: Harnessing drug-related data posted on social media in real-time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study aimed to develop a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19--related drugs. Methods: This study constructed a full pipeline for COVID-19--related drug tweet analysis, using pretrained language model--based NLP techniques as the backbone. This pipeline is architecturally composed of 4 core modules: named entity recognition and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names for time trend analysis, target sentiment analysis to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to dig potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to the COVID-19 pandemic and drug therapies between February 1, 2020, and April 30, 2022. Results: From a dataset comprising 169,659,956 COVID-19--related tweets from 103,682,686 users, our named entity recognition model identified 2,124,757 relevant tweets sourced from 1,800,372 unique users, and the top 5 most-discussed drugs: ivermectin, hydroxychloroquine, remdesivir, zinc, and vitamin D. Time trend analysis revealed that the public focused mostly on repurposed drugs (ie, hydroxychloroquine and ivermectin), and least on remdesivir, the only officially approved drug among the 5. Sentiment analysis of the top 5 most-discussed drugs revealed that public perception was predominantly shaped by celebrity endorsements, media hot spots, and governmental directives rather than empirical evidence of drug efficacy. Topic analysis obtained 15 general topics of overall drug-related tweets, with ``clinical treatment effects of drugs'' and ``physical symptoms'' emerging as the most frequently discussed topics. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use. Conclusions: This study shows that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media--based public health analytics. ", doi="10.2196/63755", url="https://www.jmir.org/2025/1/e63755", url="http://www.ncbi.nlm.nih.gov/pubmed/40053730" } @Article{info:doi/10.2196/63740, author="Wu, Peng and Hurst, H. Jillian and French, Alexis and Chrestensen, Michael and Goldstein, A. Benjamin", title="Linking Electronic Health Record Prescribing Data and Pharmacy Dispensing Records to Identify Patient-Level Factors Associated With Psychotropic Medication Receipt: Retrospective Study", journal="JMIR Med Inform", year="2025", month="Mar", day="4", volume="13", pages="e63740", keywords="electronic health records", keywords="pharmacy dispensing", keywords="psychotropic medications", keywords="prescriptions", keywords="predictive modeling", abstract="Background: Pharmacoepidemiology studies using electronic health record (EHR) data typically rely on medication prescriptions to determine which patients have received a medication. However, such data do not affirmatively indicate whether these prescriptions have been filled. External dispensing databases can bridge this information gap; however, few established methods exist for linking EHR data and pharmacy dispensing records. Objective: We described a process for linking EHR prescribing data with pharmacy dispensing records from Surescripts. As a use case, we considered the prescriptions and resulting fills for psychotropic medications among pediatric patients. We evaluated how dispensing information affects identifying patients receiving prescribed medications and assessing the association between filling prescriptions and subsequent health behaviors. Methods: This retrospective study identified all new psychotropic prescriptions to patients younger than 18 years of age at Duke University Health System in 2021. We linked dispensing to prescribing data using proximate dates and matching codes between RxNorm concept unique identifiers and National Drug Codes. We described demographic, clinical, and service use characteristics to assess differences between patients who did versus did not fill prescriptions. We fit a least absolute shrinkage and selection operator (LASSO) regression model to evaluate the predictability of a fill. We then fit time-to-event models to assess the association between whether a patient filled a prescription and a future provider visit. Results: We identified 1254 pediatric patients with a new psychotropic prescription. In total, 976 (77.8\%) patients filled their prescriptions within 30 days of their prescribing encounters. Thus, we set 30 days as a cut point for defining a valid prescription fill. Patients who filled prescriptions differed from those who did not in several key factors. Those who did not fill had slightly higher BMIs, lived in more disadvantaged neighborhoods, were more likely to have public insurance or self-pay, and included a higher proportion of male patients. Patients with prior well-child visits or prescriptions from primary care providers were more likely to fill. Additionally, patients with anxiety diagnoses and those prescribed selective serotonin reuptake inhibitors were more likely to fill prescriptions. The LASSO model achieved an area under the receiver operator characteristic curve of 0.816. The time to the follow-up visit with the same provider was censored at 90 days after the initial encounter. Patients who filled prescriptions showed higher levels of follow-up visits. The marginal hazard ratio of a follow-up visit with the same provider was 1.673 (95\% CI 1.463?1.913) for patients who filled their prescriptions. Using the LASSO model as a propensity-based weight, we calculated the weighted hazard ratio as 1.447 (95\% CI 1.257?1.665). Conclusions: Systematic differences existed between patients who did versus did not fill prescriptions. Incorporating external dispensing databases into EHR-based studies informs medication receipt and associated health outcomes. ", doi="10.2196/63740", url="https://medinform.jmir.org/2025/1/e63740" } @Article{info:doi/10.2196/55391, author="Naughton, D. Bernard", title="Planned Behavior in the United Kingdom and Ireland Online Medicine Purchasing Context: Mixed Methods Survey Study", journal="JMIR Form Res", year="2025", month="Feb", day="21", volume="9", pages="e55391", keywords="planned behavior", keywords="consumer behavior", keywords="perceived behavioral control", keywords="attitudes", keywords="online purchasing", keywords="medicine", abstract="Background: Online medicine purchasing is a growing health care opportunity. However, there is a scarcity of available evidence through a behavioral lens, which addresses why consumers buy medicines online. Governments try to influence online medicine purchasing behavior using health campaigns. However, there are little data regarding specific online medicine purchasing behaviors to support these campaigns. Objective: The theory of planned behavior explains that perceived behavioral control (PBC), attitudes, and norms contribute to intentions, leading to behaviors. This study challenges these assumptions, by testing them in an online medicine purchasing context. We asked: What is the role of attitudes, norms, and PBC in an online medicine purchasing context. Methods: An anonymous online snowball convenience sample survey, including open and closed questions concerning online medicine purchasing, was implemented. The data were thematically analyzed until data saturation. The emerging themes were applied to each individual response, as part of a case-by-case narrative analysis. Results: Of the 190 consumers from the United Kingdom and Ireland who consented to participate in the study, 46 participants had purchased medicines online, 9 of which were illegal sales. Of the 113 participants who demonstrated an intention to purchase, 42 (37.2\%) completed a purchase. There were many cases in which participants demonstrated an intention to buy medicines online, but this intention did not translate to a purchasing behavior (71/190, 37.4\%). Reasons for consumers progressing from intention to behavior are suggested to be impacted by PBC and attitudes. Qualitative data identified access to medicine as a factor encouraging online medicine purchasing behaviors and a facilitator of behavior transition. Despite understanding the importance of why some medicines required a prescription, which is described as an example of legal and health norms, and despite suspicion and concern categorized as negative attitudes in this paper, some participants were still buying products illegally online. Risk reduction strategies were performed by 17 participants (17/190, 9\%). These strategies facilitated a transition from intention to behavior. Conclusions: The study results indicate that a consumer's intention to buy does not automatically translate to a purchasing behavior online; instead, a transition phase exists. Second, consumers followed different pathways to purchase and used risk reduction practices while transitioning from an intention to a behavior. Finally, owing to the covert nature of online medicine purchasing, norms do not appear to be as influential as PBC and attitudes in an online medicine purchasing setting. Understanding how a consumer transitions from an intention to a behavior could be useful for researchers, health care professionals, and policymakers involved in public health campaigns. We encourage future research to focus on different consumer behavior pathways or ideal types, rather than taking a blanket approach to public health campaigns. ", doi="10.2196/55391", url="https://formative.jmir.org/2025/1/e55391" } @Article{info:doi/10.2196/63987, author="Pironet, Antoine and Phillips, Alison L. and Vrijens, Bernard", title="Correlation Between Objective Habit Metrics and Objective Medication Adherence: Retrospective Study of 15,818 Participants From Clinical Studies", journal="Interact J Med Res", year="2025", month="Feb", day="6", volume="14", pages="e63987", keywords="medication adherence", keywords="compliance", keywords="habit", keywords="history", keywords="correlation", keywords="association", keywords="intake", keywords="electronic database", keywords="retrospective", keywords="medication", keywords="drug", keywords="adherence", abstract="Background: Medication adherence, or how patients take their medication as prescribed, is suboptimal worldwide. Improving medication-taking habit might be an effective way to improve medication adherence. However, habit is difficult to quantify, and conventional habit metrics are self-reported, with recognized limitations. Recently, several objective habit metrics have been proposed, based on objective medication-taking data. Objective: We aim to explore the correlation between objective habit metrics and objective medication adherence on a large dataset. Methods: The Medication Event Monitoring System Adherence Knowledge Center, a database of anonymized electronic medication intake data from ambulant participants enrolled in past clinical studies, was used as the data source. Electronic medication intake data from participants following a once-daily regimen and monitored for 14 days or more were used. Further, two objective habit metrics were computed from each participant's medication intake history: (1) SD of the hour of intake, representing daily variability in the timing of medication intakes, and (2) weekly cross-correlation, representing weekly consistency in the timing of medication intakes. The implementation component of medication adherence was quantified using (1) the proportion of doses taken and (2) the proportion of correct days. Results: A total of 15,818 participants met the criteria. These participants took part in 108 clinical studies mainly focused on treatments for hypertension (n=4737, 30\%) and osteoporosis (n=3353, 21\%). The SD of the hour of intake was significantly negatively correlated with the 2 objective adherence metrics: proportion of correct days (Spearman correlation coefficient, $\rho$S=--0.62, P<.001) and proportion of doses taken ($\rho$S=--0.09, P<.001). The weekly cross-correlation was significantly positively correlated with the 2 objective adherence metrics: proportion of correct days ($\rho$S=0.55, P<.001) and proportion of doses taken ($\rho$S=0.32, P<.001). A lower daily or weekly variability in the timing of medication intakes is thus associated with better medication adherence. However, no variability is not the norm, as only 3.6\% of participants have 95\% of their intakes in a 1-hour window. Among the numerous factors influencing medication adherence, habit strength is an important one as it explains over 30\% of the variance in medication adherence. Conclusions: Objective habit metrics are correlated to objective medication adherence. Such objective habit metrics can be used to monitor patients and identify those who may benefit from habit-building support. ", doi="10.2196/63987", url="https://www.i-jmr.org/2025/1/e63987" } @Article{info:doi/10.2196/65546, author="Insani, Norma Widya and Zakiyah, Neily and Puspitasari, Melyani Irma and Permana, Yorga Muhammad and Parmikanti, Kankan and Rusyaman, Endang and Suwantika, Abdurrohim Auliya", title="Digital Health Technology Interventions for Improving Medication Safety: Systematic Review of Economic Evaluations", journal="J Med Internet Res", year="2025", month="Feb", day="5", volume="27", pages="e65546", keywords="digital health technology", keywords="drug safety", keywords="adverse drug events", keywords="medication errors", keywords="patient safety", abstract="Background: Medication-related harm, including adverse drug events (ADEs) and medication errors, represents a significant iatrogenic burden in clinical care. Digital health technology (DHT) interventions can significantly enhance medication safety outcomes. Although the clinical effectiveness of DHT for medication safety has been relatively well studied, much less is known about the cost-effectiveness of these interventions. Objective: This study aimed to systematically review the economic impact of DHT interventions on medication safety and examine methodological challenges to inform future research directions. Methods: A systematic search was conducted across 3 major electronic databases (ie, PubMed, Scopus, and EBSCOhost). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for this systematic review. Two independent investigators conducted a full-text review after screening preliminary titles and abstracts. We adopted recommendations from the Panel on Cost-Effectiveness in Health and Medicine for data extraction. A narrative analysis was conducted to synthesize clinical and economic outcomes. The quality of reporting for the included studies was assessed using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Results: We included 13 studies that assessed the cost-effectiveness (n=9, 69.2\%), cost-benefit (n=3, 23.1\%), and cost-utility (n=1, 7.7\%) of DHT for medication safety. Of the included studies, more than half (n=7, 53.9\%) evaluated a clinical decision support system (CDSS)/computerized provider order entry (CPOE), 4 (30.8\%) examined automated medication-dispensing systems, and 2 (15.4\%) focused on pharmacist-led outreach programs targeting health care professionals. In 12 (92.3\% ) studies, DHT was either cost-effective or cost beneficial compared to standard care. On average, DHT interventions reduced ADEs by 37.12\% (range 8.2\%-66.5\%) and medication errors by 54.38\% (range 24\%-83\%). The key drivers of cost-effectiveness included reductions in outcomes, the proportion of errors resulting in ADEs, and implementation costs. Despite a significant upfront cost, DHT showed a return on investment within 3-4.25 years due to lower cost related with ADE treatment and improved workflow efficiency. In terms of reporting quality, the studies were classified as good (n=10, 76.9\%) and moderate (n=3, 23.1\%). Key methodological challenges included short follow-up periods, the absence of alert compliance tracking, the lack of ADE and error severity categorization, and omission of indirect costs. Conclusions: DHT interventions are economically viable to improve medication safety, with a substantial reduction in ADEs and medication errors. Future studies should prioritize incorporating alert compliance tracking, ADE and error severity classification, and evaluation of indirect costs, thereby increasing clinical benefits and economic viability. ", doi="10.2196/65546", url="https://www.jmir.org/2025/1/e65546" } @Article{info:doi/10.2196/54601, author="Trevena, William and Zhong, Xiang and Alvarado, Michelle and Semenov, Alexander and Oktay, Alp and Devlin, Devin and Gohil, Yogesh Aarya and Chittimouju, Harsha Sai", title="Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study", journal="J Med Internet Res", year="2025", month="Jan", day="30", volume="27", pages="e54601", keywords="natural language processing", keywords="large language models", keywords="ChatGPT", keywords="drug discontinuation events", keywords="zero-shot classification", keywords="artificial intelligence", keywords="AI", abstract="Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored. Identifying DDEs is crucial for understanding medication adherence and patient outcomes. Objective: The aim of this study is to provide a flexible framework for investigating various clinical research questions in data-sparse environments. We provide an example of the utility of this framework by identifying DDEs and their root causes in an open-source web-based forum, MedHelp, and by releasing the first open-source DDE datasets to aid further research in this domain. Methods: We used several LLMs, including GPT-4 Turbo, GPT-4o, DeBERTa (Decoding-Enhanced Bidirectional Encoder Representations from Transformer with Disentangled Attention), and BART, among others, to detect and determine the root causes of DDEs in user comments posted on MedHelp. Our study design included the use of zero-shot classification, which allows these models to make predictions without task-specific training. We split user comments into sentences and applied different classification strategies to assess the performance of these models in identifying DDEs and their root causes. Results: Among the selected models, GPT-4o performed the best at determining the root causes of DDEs, predicting only 12.9\% of root causes incorrectly (hamming loss). Among the open-source models tested, BART demonstrated the best performance in detecting DDEs, achieving an F1-score of 0.86, a false positive rate of 2.8\%, and a false negative rate of 6.5\%, all without any fine-tuning. The dataset included 10.7\% (107/1000) DDEs, emphasizing the models' robustness in an imbalanced data context. Conclusions: This study demonstrated the effectiveness of open- and closed-source LLMs, such as GPT-4o and BART, for detecting DDEs and their root causes from publicly accessible data through zero-shot classification. The robust and scalable framework we propose can aid researchers in addressing data-sparse clinical research questions. The launch of open-access DDE datasets has the potential to stimulate further research and novel discoveries in this field. ", doi="10.2196/54601", url="https://www.jmir.org/2025/1/e54601", url="http://www.ncbi.nlm.nih.gov/pubmed/39883487" } @Article{info:doi/10.2196/54148, author="Li, Suya and Chen, Hui-Jun and Zhou, Jie and Zhouchen, Yi-Bei and Wang, Rong and Guo, Jinyi and Redding, R. Sharon and Ouyang, Yan-Qiong", title="Effectiveness of a Web-Based Medication Education Course on Pregnant Women's Medication Information Literacy and Decision Self-Efficacy: Randomized Controlled Trial", journal="J Med Internet Res", year="2025", month="Jan", day="22", volume="27", pages="e54148", keywords="decision self-efficacy", keywords="self-efficacy", keywords="decision efficacy", keywords="medication information literacy", keywords="information literacy", keywords="web-based medication education", keywords="medication education", keywords="web-based platforms", keywords="pregnant women", keywords="pregnancy", keywords="RCT", keywords="randomized controlled trial", abstract="Background: Medication-related adverse events are common in pregnant women, and most are due to misunderstanding medication information. The identification of appropriate medication information sources requires adequate medical information literacy (MIL). It is important for pregnant women to comprehensively evaluate the risk of medication treatment, self-monitor their medication response, and actively participate in decision-making to reduce medication-related adverse events. Objective: This study aims to examine the effectiveness of a medication education course on a web-based platform in improving pregnant women's MIL and decision self-efficacy. Methods: A randomized controlled trial was conducted. Pregnant women were recruited from January to June 2021 in the Department of Obstetrics and Gynecology of a large hospital in a major city in central China. A total of 108 participants were randomly divided into a control group (CG), which received routine prenatal care from nurses and physicians, and an intervention group (IG), which received an additional 3-week web-based medication education course based on the theory of planned behavior as part of routine prenatal care. Participants completed a Medication Information Literacy Scale and a decision self-efficacy questionnaire at baseline, upon completion of the intervention, and at a 4-week follow-up. Generalized estimation equations (GEE) were used to analyze the main effect (time and grouping) and interaction effect (grouping{\texttimes}time) of the 2 outcomes. The CONSORT-EHEALTH (V 1.6.1) checklist was used to guide the reporting of this randomized controlled trial. Results: A total of 91 pregnant women (48 in the IG and 43 in the CG) completed the questionnaires at the 3 time points. The results of GEE indicated that there was no statistically significant difference in time{\texttimes}group interactions of MIL between the 2 groups (F2=3.12; P=.21). The results of the main effect analysis showed that there were statistically significant differences in MIL between the 2 groups at T1 and T2 (F1=17.79; P<.001). Moreover, the results of GEE indicated that there was a significant difference in decision self-efficacy regarding the time factor, grouping factor, and time{\texttimes}group interactions (F2=21.98; P<.001). The results of the simple effect analysis indicated a statistically significant difference in decision self-efficacy between the 2 groups at T1 (F1=36.29; P<.001) and T2 (F1=36.27; P<.001) compared to T0. Results showed that MIL and decision self-efficacy in the IG were found to be significantly higher than those in the CG (d=0.81; P<.001 and d=1.26; P<.001, respectively), and they remained significantly improved at the 4-week follow-up (d=0.59; P<.001 and d=1.27; P<.001, respectively). Conclusions: Web-based medication education courses based on the theory of planned behavior can effectively improve pregnant women's MIL and decision self-efficacy, and they can be used as supplementary education during routine prenatal care. Trial Registration: Chinese Clinical Trial Registry ChiCTR2100041817; https://www.chictr.org.cn/showproj.html?proj=66685 ", doi="10.2196/54148", url="https://www.jmir.org/2025/1/e54148" } @Article{info:doi/10.2196/60084, author="Gebreyohannes, Alemayehu Eyob and Thornton, Christopher and Thiessen, Myra and de Vries, T. Sieta and Q Andrade, Andre and Kalisch Ellett, Lisa and Frank, Oliver and Cheah, Yeong Phaik and Choo, Raymond Kim-Kwang and Laba, Lea Tracey and Roughead, E. Elizabeth and Hwang, Indae and Moses, Geraldine and Lim, Renly", title="Co-Designing a Consumer-Focused Digital Reporting Health Platform to Improve Adverse Medicine Event Reporting: Protocol for a Multimethod Research Project (the ReMedi Project)", journal="JMIR Res Protoc", year="2025", month="Jan", day="15", volume="14", pages="e60084", keywords="adverse drug events", keywords="drug-related side effects and adverse reactions", keywords="adverse drug reaction reporting systems", keywords="pharmacovigilance", keywords="digital health", keywords="medication safety", keywords="co-design", keywords="qualitative research, user-centered design", abstract="Background: Adverse medicine events (AMEs) are unintended effects that occur following administration of medicines. Up to 70\% of AMEs are not reported to, and hence remain undetected by, health care professionals and only 6\% of AMEs are reported to regulators. Increased reporting by consumers, health care professionals, and pharmaceutical companies to medicine regulatory authorities is needed to increase the safety of medicines. Objective: We describe a project that aims to co-design a digital reporting platform to improve detection and management of AMEs by consumers and health care professionals and improve reporting to regulators. Methods: The project will be conducted in 3 phases and uses a co-design methodology that prioritizes equity in designing with stakeholders. Our project is guided by the Consolidated Framework for Implementation Research. In phase 1, we will engage with 3 stakeholder groups---consumers, health care professionals, and regulators---to define digital platform development standards. We will conduct a series of individual interviews, focus group discussions, and co-design workshops with the stakeholder groups. In phase 2, we will work with a software developer and user interaction design experts to prototype, test, and develop the digital reporting platform based on findings from phase 1. In phase 3, we will implement and trial the digital reporting platform in South Australia through general practices and pharmacies. Consumers who have recently started using medicines new to them will be recruited to use the digital reporting platform to report any apparent, suspected, or possible AMEs since starting the new medicine. Process and outcome evaluations will be conducted to assess the implementation process and to determine whether the new platform has increased AME detection and reporting. Results: This project, initiated in 2023, will run until 2026. Phase 1 will result in persona profiles and user journey maps that define the standards for the user-friendly platform and interactive data visualization tool or dashboard that will be developed and further improved in phase 2. Finally, phase 3 will provide insights of the implemented platform regarding its impact on AME detection, management, and reporting. Findings will be published progressively as we complete the different phases of the project. Conclusions: This project adopts a co-design methodology to develop a new digital reporting platform for AME detection and reporting, considering the perspectives and lived experience of stakeholders and addressing their requirements throughout the entire process. The overarching goal of the project is to leverage the potential of both consumers and technology to address the existing challenges of underdetection and underreporting of AMEs to health care professionals and regulators. The project potentially will improve individual patient safety and generate new data for regulatory purposes related to medicine safety and effectiveness. International Registered Report Identifier (IRRID): DERR1-10.2196/60084 ", doi="10.2196/60084", url="https://www.researchprotocols.org/2025/1/e60084" } @Article{info:doi/10.2196/53957, author="Scharf, Tamara and Huber, A. Carola and N{\"a}pflin, Markus and Zhang, Zhongxing and Khatami, Ramin", title="Trends in Prescription of Stimulants and Narcoleptic Drugs in Switzerland: Longitudinal Health Insurance Claims Analysis for the Years 2014-2021", journal="JMIR Public Health Surveill", year="2025", month="Jan", day="7", volume="11", pages="e53957", keywords="prescription trends", keywords="claims data", keywords="cross-sectional data", keywords="narcolepsy", keywords="prescribers", keywords="prescribing practices", keywords="medical care", keywords="stimulants", keywords="stimulant medication", abstract="Background: Stimulants are potent treatments for central hypersomnolence disorders or attention-deficit/hyperactivity disorders/attention deficit disorders but concerns have been raised about their potential negative consequences and their increasing prescription rates. Objective: We aimed to describe stimulant prescription trends in Switzerland from 2014 to 2021. Second, we aimed to analyze the characteristics of individuals who received stimulant prescriptions in 2021 and investigate the link between stimulant prescriptions and hospitalization rates in 2021, using hospitalization as a potential indicator of adverse health outcomes. Methods: Longitudinal and cross-sectional data from a large Swiss health care insurance were analyzed from all insureds older than 6 years. The results were extrapolated to the Swiss general population. We identified prescriptions for methylphenidate, lisdexamfetamine, modafinil, and sodium oxybate and calculated prevalences of each drug prescription over the period from 2014 to 2021. For 2021 we provide detailed information on the prescribers and evaluate the association of stimulant prescription and the number and duration of hospitalization using logistic regression models. Results: We observed increasing prescription rates of all stimulants in all age groups from 2014 to 2021 (0.55\% to 0.81\%, 43,848 to 66,113 insureds with a prescription). In 2021, 37.1\% (28,057 prescriptions) of the medications were prescribed by psychiatrists, followed by 36.1\% (n=27,323) prescribed by general practitioners and 1\% (n=748) by neurologists. Only sodium oxybate, which is highly specific for narcolepsy treatment, was most frequently prescribed by neurologists (27.8\%, 37 prescriptions). Comorbid psychiatric disorders were common in patients receiving stimulants. Patients hospitalized in a psychiatric institution were 5.3 times (odds ratio 5.3, 95\% CI 4.63?6.08, P<.001) more likely to have a stimulant prescription than those without hospitalization. There were no significant associations between stimulant prescription and the total length of inpatient stay (odds ratio 1, 95\% CI 1?1, P=.13). Conclusions: The prescription of stimulant medication in Switzerland increased slightly but continuously over years, but at lower rates compared to the estimated prevalence of central hypersomnolence disorders and attention-deficit/hyperactivity disorders/attention deficit disorders. Most stimulants are prescribed by psychiatrists, closely followed by general practitioners. The increased odds for hospitalization to psychiatric institutions for stimulant receivers reflects the severity of disease and the higher psychiatric comorbidities in these patients. ", doi="10.2196/53957", url="https://publichealth.jmir.org/2025/1/e53957" } @Article{info:doi/10.2196/57824, author="Dimitsaki, Stella and Natsiavas, Pantelis and Jaulent, Marie-Christine", title="Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review", journal="J Med Internet Res", year="2024", month="Dec", day="30", volume="26", pages="e57824", keywords="pharmacovigilance", keywords="drug safety", keywords="artificial intelligence", keywords="machine learning", keywords="real-world data", keywords="scoping review", abstract="Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology. Objective: This scoping review depicts the emerging use of AI on structured RWD for pharmacovigilance purposes to identify relevant trends and potential research gaps. Methods: The scoping review methodology is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We queried the MEDLINE database through the PubMed search engine. Relevant scientific manuscripts published from January 2010 to January 2024 were retrieved. The included studies were ``mapped'' against a set of evaluation criteria, including applied AI approaches, code availability, description of the data preprocessing pipeline, clinical validation of AI models, and implementation of trustworthy AI criteria following the guidelines of the FUTURE (Fairness, Universality, Traceability, Usability, Robustness, and Explainability)-AI initiative. Results: The scoping review ultimately yielded 36 studies. There has been a significant increase in relevant studies after 2019. Most of the articles focused on adverse drug reaction detection procedures (23/36, 64\%) for specific adverse effects. Furthermore, a substantial number of studies (34/36, 94\%) used nonsymbolic AI approaches, emphasizing classification tasks. Random forest was the most popular machine learning approach identified in this review (17/36, 47\%). The most common RWD sources used were electronic health care records (28/36, 78\%). Typically, these data were not available in a widely acknowledged data model to facilitate interoperability, and they came from proprietary databases, limiting their availability for reproducing results. On the basis of the evaluation criteria classification, 10\% (4/36) of the studies published their code in public registries, 16\% (6/36) tested their AI models in clinical environments, and 36\% (13/36) provided information about the data preprocessing pipeline. In addition, in terms of trustworthy AI, 89\% (32/36) of the studies followed at least half of the trustworthy AI initiative guidelines. Finally, selection and confounding biases were the most common biases in the included studies. Conclusions: AI, along with structured RWD, constitutes a promising line of work for drug safety and pharmacovigilance. However, in terms of AI, some approaches have not been examined extensively in this field (such as explainable AI and causal AI). Moreover, it would be helpful to have a data preprocessing protocol for RWD to support pharmacovigilance processes. Finally, because of personal data sensitivity, evaluation procedures have to be investigated further. ", doi="10.2196/57824", url="https://www.jmir.org/2024/1/e57824" } @Article{info:doi/10.2196/53424, author="Daluwatte, Chathuri and Khromava, Alena and Chen, Yuning and Serradell, Laurence and Chabanon, Anne-Laure and Chan-Ou-Teung, Anthony and Molony, Cliona and Juhaeri, Juhaeri", title="Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media Content: Algorithm Development and Validation Study", journal="JMIR Infodemiology", year="2024", month="Dec", day="20", volume="4", pages="e53424", keywords="adverse event", keywords="COVID-19", keywords="detection", keywords="large language model", keywords="mass vaccination", keywords="natural language processing", keywords="pharmacovigilance", keywords="safety", keywords="social media", keywords="vaccine", abstract="Background: Spontaneous pharmacovigilance reporting systems are the main data source for signal detection for vaccines. However, there is a large time lag between the occurrence of an adverse event (AE) and the availability for analysis. With global mass COVID-19 vaccination campaigns, social media, and web content, there is an opportunity for real-time, faster monitoring of AEs potentially related to COVID-19 vaccine use. Our work aims to detect AEs from social media to augment those from spontaneous reporting systems. Objective: This study aims to monitor AEs shared in social media and online support groups using medical context-aware natural language processing language models. Methods: We developed a language model--based web app to analyze social media, patient blogs, and forums (from 190 countries in 61 languages) around COVID-19 vaccine--related keywords. Following machine translation to English, lay language safety terms (ie, AEs) were observed using the PubmedBERT-based named-entity recognition model (precision=0.76 and recall=0.82) and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms using knowledge graphs (MedDRA terminology is an internationally used set of terms relating to medical conditions, medicines, and medical devices that are developed and registered under the auspices of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use). Weekly and cumulative aggregated AE counts, proportions, and ratios were displayed via visual analytics, such as word clouds. Results: Most AEs were identified in 2021, with fewer in 2022. AEs observed using the web app were consistent with AEs communicated by health authorities shortly before or within the same period. Conclusions: Monitoring the web and social media provides opportunities to observe AEs that may be related to the use of COVID-19 vaccines. The presented analysis demonstrates the ability to use web content and social media as a data source that could contribute to the early observation of AEs and enhance postmarketing surveillance. It could help to adjust signal detection strategies and communication with external stakeholders, contributing to increased confidence in vaccine safety monitoring. ", doi="10.2196/53424", url="https://infodemiology.jmir.org/2024/1/e53424", url="http://www.ncbi.nlm.nih.gov/pubmed/39705077" } @Article{info:doi/10.2196/60535, author="Jeanmougin, Pauline and Larramendy, St{\'e}phanie and Fournier, Jean-Pascal and Gaultier, Aur{\'e}lie and Rat, C{\'e}dric", title="Effect of a Feedback Visit and a Clinical Decision Support System Based on Antibiotic Prescription Audit in Primary Care: Multiarm Cluster-Randomized Controlled Trial", journal="J Med Internet Res", year="2024", month="Dec", day="18", volume="26", pages="e60535", keywords="antibacterial agents", keywords="feedback", keywords="clinical decision support system", keywords="prescriptions", keywords="primary health care", keywords="clinical decision", keywords="antibiotic prescription", keywords="antimicrobial", keywords="antibiotic stewardship", keywords="interventions", keywords="health insurance", keywords="systematic antibiotic prescriptions", abstract="Background: While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is needed to optimize future interventions. Objective: This study aimed to evaluate 2 multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care. Methods: An open-label, cluster-randomized controlled trial of 2501 general practitioners (GPs) working in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention, consisting of a visit by a health insurance representative who gave prescription feedback and provided a leaflet for treating cystitis and tonsillitis; and a clinical decision support system (CDSS)--based intervention, consisting of a visit with prescription feedback and a CDSS demonstration on antibiotic prescribing. The control group received no intervention. Data on systemic antibiotic dispensing was obtained from the National Health Insurance System (Syst{\`e}me National d'Information Inter-R{\'e}gimes de l'Assurance Maladie) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using a 2-level hierarchical analysis of covariance adjusted for annual antibiotic prescription volume at baseline. Results: Overall, 2501 GPs were randomized (n=1099, 43.9\% women). At 12 months, the mean volume of systemic antibiotics per GP decreased by 219.2 (SD 61.4; 95\% CI ?339.5 to ?98.8; P<.001) defined daily doses in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systemic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (?109.7, SD 62.4; 95\% CI ?232.0 to 12.5 defined daily doses; P=.08). Conclusions: A visit by a health insurance representative combining feedback and a CDSS demonstration resulted in a 4.4\% (-219.2/4930) reduction in the total volume of systemic antibiotic prescriptions in 12 months. Trial Registration: ClinicalTrials.gov NCT04028830; https://clinicaltrials.gov/study/NCT04028830 ", doi="10.2196/60535", url="https://www.jmir.org/2024/1/e60535", url="http://www.ncbi.nlm.nih.gov/pubmed/39693139" } @Article{info:doi/10.2196/57687, author="Orr, Noreen and Rogers, Morwenna and Stein, Abigail and Thompson Coon, Jo and Stein, Kenneth", title="Reviewing the Evidence Base for Topical Steroid Withdrawal Syndrome in the Research Literature and Social Media Platforms: An Evidence Gap Map", journal="J Med Internet Res", year="2024", month="Dec", day="6", volume="26", pages="e57687", keywords="topical steroid withdrawal syndrome", keywords="evidence gap map", keywords="social media", keywords="blogs", keywords="Instagram", keywords="Reddit", keywords="topical corticosteroids", abstract="Background: Within the dermatological community, topical steroid withdrawal syndrome (TSWS) is a medically contested condition with a limited research base. Published studies on TSWS indicate that it is a distinct adverse effect of prolonged use of topical corticosteroids, but there is a paucity of high-quality research evidence. Among the ``patient community,'' awareness has been increasing, with rapid growth in social media posts on TSWS and the introduction of online communities such as the International Topical Steroid Awareness Network. This evidence gap map (EGM) was developed in response to recent calls for research to better understand TSWS and aims to be an important resource to guide both researchers and clinicians in the prioritization of research topics for further research. Objective: This study aims to identify the range, extent, and type of evidence on TSWS in the research literature and social media platforms using an EGM. Methods: The MEDLINE and Embase (Ovid), CINAHL (EBSCOhost), and ProQuest Dissertations \& Theses and Conference Proceedings Citation Index (CPCI-Science and CPCI-Social Science \& Humanities via Web of Science) databases were searched. The final search was run in November 2023. Study titles, abstracts, and full texts were screened by 2 reviewers, and a third was consulted to resolve any differences. Blogging sites WordPress, Medium, and Blogspot and Google were searched; Instagram and Reddit were searched for the 100 most recent posts on specific dates in February 2023. Blog titles, Instagram posts, and Reddit posts were screened for relevance by 2 reviewers. A data extraction tool was developed on EPPI-Reviewer, and data extraction was undertaken by one reviewer and checked by a second; any inconsistencies were resolved through discussion. We did not undertake quality appraisal of the included studies. EPPI-Reviewer and EPPI-Mapper were used to generate the interactive EGM. Results: Overall, 81 academic publications and 223 social media posts were included in the EGM. The research evidence mainly addressed the physical symptoms of TSWS (skin), treatments, and, to a lesser extent, risk factors and disease mechanisms. The social media evidence primarily focused on the physical symptoms (skin and nonskin), mental health symptoms, relationships, activities of everyday living, beliefs and attitudes, and treatments. Conclusions: The EGM shows that research evidence is growing on TSWS but remains lacking in several important areas: longer-term prospective observational studies to assess the safety of prolonged use of topical corticosteroids and to prevent addiction; qualitative research to understand the lived experience of TSWS; and longitudinal research on the patient's ``TSWS journey'' to healing. The inclusion of social media evidence is a methodological innovation in EGMs, recognizing the increased presence of \#topicalsteroidwithdrawal on social media and how it can be used to better understand the patient perspective and ultimately, provide better care for people with TSWS. ", doi="10.2196/57687", url="https://www.jmir.org/2024/1/e57687" } @Article{info:doi/10.2196/55185, author="Van De Sijpe, Greet and Gijsen, Matthias and Van der Linden, Lorenz and Strouven, Stephanie and Simons, Eline and Martens, Emily and Persan, Nele and Grootaert, Veerle and Foulon, Veerle and Casteels, Minne and Verelst, Sandra and Vanbrabant, Peter and De Winter, Sabrina and Spriet, Isabel", title="A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study", journal="J Med Internet Res", year="2024", month="Nov", day="27", volume="26", pages="e55185", keywords="medication reconciliation", keywords="medication discrepancy", keywords="emergency department", keywords="prediction model", keywords="risk stratification", keywords="MED-REC predictor", keywords="MED-REC", keywords="predictor", keywords="patient", keywords="medication", keywords="hospital", keywords="software-implemented prediction model", keywords="software", keywords="geographic validation", keywords="geographic", abstract="Background: Many patients do not receive a comprehensive medication reconciliation, mostly owing to limited resources. We hence need an approach to identify those patients at the emergency department (ED) who are at increased risk for clinically relevant discrepancies. Objective: The aim of our study was to develop and externally validate a prediction model to identify patients at risk for at least 1 clinically relevant medication discrepancy upon ED presentation. Methods: A prospective, multicenter, observational study was conducted at the University Hospitals Leuven and General Hospital Sint-Jan Brugge-Oostende AV, Belgium. Medication histories were obtained from patients admitted to the ED between November 2017 and May 2022, and clinically relevant medication discrepancies were identified. Three distinct datasets were created for model development, temporal external validation, and geographic external validation. Multivariable logistic regression with backward stepwise selection was used to select the final model. The presence of at least 1 clinically relevant discrepancy was the dependent variable. The model was evaluated by measuring calibration, discrimination, classification, and net benefit. Results: We included 824, 350, and 119 patients in the development, temporal validation, and geographic validation dataset, respectively. The final model contained 8 predictors, for example, age, residence before admission, number of drugs, and number of drugs of certain drug classes based on Anatomical Therapeutic Chemical coding. Temporal validation showed excellent calibration with a slope of 1.09 and an intercept of 0.18. Discrimination was moderate with a c-index (concordance index) of 0.67 (95\% CI 0.61-0.73). In the geographic validation dataset, the calibration slope and intercept were 1.35 and 0.83, respectively, and the c-index was 0.68 (95\% CI 0.58-0.78). The model showed net benefit over a range of clinically reasonable threshold probabilities and outperformed other selection criteria. Conclusions: Our software-implemented prediction model shows moderate performance, outperforming random or typical selection criteria for medication reconciliation. Depending on available resources, the probability threshold can be customized to increase either the specificity or the sensitivity of the model. ", doi="10.2196/55185", url="https://www.jmir.org/2024/1/e55185" } @Article{info:doi/10.2196/45289, author="Lee, Chung-Chun and Lee, Seunghee and Song, Mi-Hwa and Kim, Jong-Yeup and Lee, Suehyun", title="Bidirectional Long Short-Term Memory--Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches", journal="JMIR Med Inform", year="2024", month="Nov", day="20", volume="12", pages="e45289", keywords="adverse drug reaction", keywords="social network service", keywords="classification model", keywords="Korean text data", keywords="social networking service", keywords="drug detection", keywords="deep learning", keywords="Korea", keywords="social data", keywords="older", keywords="older adults", keywords="drug surveillance", keywords="medicine", abstract="Background: Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English. Objective: A cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network. Methods: In previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, caf{\'e} posts, and NAVER Q\&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac. Results: Among the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85\% and 80\% accuracy, respectively. Conclusions: Here, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible. ", doi="10.2196/45289", url="https://medinform.jmir.org/2024/1/e45289" } @Article{info:doi/10.2196/64674, author="Smith, N. Shawna and Lanham, M. Michael S. and Seagull, Jacob F. and Fabbri, Morris and Dorsch, P. Michael and Jennings, Kathleen and Barnes, Geoffrey", title="System-Wide, Electronic Health Record--Based Medication Alerts for Appropriate Prescribing of Direct Oral Anticoagulants: Pilot Randomized Controlled Trial", journal="JMIR Form Res", year="2024", month="Nov", day="8", volume="8", pages="e64674", keywords="direct oral anticoagulants", keywords="electronic health record", keywords="medication safety", keywords="prescribing errors", keywords="pilot randomized controlled trial", keywords="alert system optimization", keywords="clinical decision support", keywords="EHR", keywords="randomized controlled trial", keywords="RCT", keywords="oral anticoagulants", abstract="Background: While direct oral anticoagulants (DOACs) have improved oral anticoagulation management, inappropriate prescribing remains prevalent and leads to adverse drug events. Antithrombotic stewardship programs seek to enhance DOAC prescribing but require scalable and sustainable strategies. Objective: We present a pilot, prescriber-level randomized controlled trial to assess the effectiveness of electronic health record (EHR)--based medication alerts in a large health system. Methods: The pilot assessed prescriber responses to alerts for initial DOAC prescription errors (apixaban and rivaroxaban). A user-centered, multistage design process informed alert development, emphasizing clear indication, appropriate dosing based on renal function, and drug-drug interactions. Alerts appeared whenever a DOAC was being prescribed in a way that did not follow package label instructions. Clinician responses measured acceptability, accuracy, feasibility, and utilization of the alerts. Results: The study ran from August 1, 2022, through April 30, 2023. Only 1 prescriber requested trial exclusion, demonstrating acceptability. The error rate for false alerts due to incomplete data was 6.6\% (16/243). Two scenarios with alert design and/or execution errors occurred but were quickly identified and resolved, underlining the importance of a responsive quality assurance process in EHR-based interventions. Trial feasibility issues related to alert-data capture were identified and resolved. Trial feasibility was also assessed with balanced randomization of prescribers and the inclusion of various alerts across both medications. Assessing utilization, 34.2\% (83/243) of the encounters (with 134 prescribers) led to a prescription change. Conclusions: The pilot implementation study demonstrated the acceptability, accuracy, feasibility, and estimates of the utilization of EHR-based medication alerts for DOAC prescriptions and successfully established just-in-time randomization of prescribing clinicians. This pilot study sets the stage for large-scale, randomized implementation evaluations of EHR-based alerts to improve medication safety. Trial Registration: ClinicalTrials.gov NCT05351749; https://clinicaltrials.gov/study/NCT05351749 ", doi="10.2196/64674", url="https://formative.jmir.org/2024/1/e64674" } @Article{info:doi/10.2196/64446, author="Winter, D. Jonathan and Kerns, William J. and Brandt, Nicole and Wastila, Linda and Qato, Danya and Sabo, T. Roy and Petterson, Stephen and Chung, YoonKyung and Reves, Sarah and Winter, Christopher and Winter, M. Katherine and Elonge, Eposi and Ewasiuk, Craig and Fu, Yu-Hua and Funk, Adam and Krist, Alex and Etz, Rebecca", title="Prescribing Trends and Associated Outcomes of Antiepileptic Drugs and Other Psychotropic Medications in US Nursing Homes: Proposal for a Mixed Methods Investigation", journal="JMIR Res Protoc", year="2024", month="Sep", day="19", volume="13", pages="e64446", keywords="Alzheimer disease", keywords="dementia", keywords="antiepileptic drug", keywords="antiseizure medication", keywords="antipsychotic", keywords="National Partnership", keywords="nursing home", keywords="mood stabilizer", keywords="COVID-19", abstract="Background: Pilot data suggest that off-label, unmonitored antiepileptic drug prescribing for behavioral and psychological symptoms of dementia is increasing, replacing other psychotropic medications targeted by purposeful reduction efforts. This trend accelerated during the COVID-19 pandemic. Although adverse outcomes related to this trend remain unknown, preliminary results hint that harms may be increasing and concentrated in vulnerable populations. Objective: Using a mixed methods approach including both a retrospective secondary data analysis and a national clinician survey, this study aims to describe appropriate and potentially inappropriate antiepileptic and other psychoactive drug prescribing in US nursing homes (NHs), characteristics and patient-oriented outcomes associated with this prescribing, and how these phenomena may be changing under the combined stressors of the COVID-19 pandemic and the pressure of reduction initiatives. Methods: To accomplish the objective, resident-level, mixed-effects regression models and interrupted time-series analyses will draw on cohort elements linked at an individual level from the Centers for Medicare and Medicaid Services' (CMS) Minimum Data Set, Medicare Part D, Medicare Provider Analysis and Review, and Outpatient and Public Use Files. Quarterly cohorts of NH residents (2009-2021) will incorporate individual-level data, including demographics; health status; disease variables; psychotropic medication claims; comprehensive NH health outcomes; hospital and emergency department adverse events; and NH details, including staffing resources and COVID-19 statistics. To help explain and validate findings, we will conduct a national qualitative survey of NH prescribers regarding their knowledge and beliefs surrounding changing approaches to dementia care and associated outcomes. Results: Funding was obtained in September 2022. Institutional review board exemption approval was obtained in January 2023. The CMS Data Use Agreement was submitted in May 2023 and signed in March 2024. Data access was obtained in June 2024. Cohort creation is anticipated by January 2025, with crosswalks finalized by July 2025. The first survey was fielded in October 2023 and published in July 2024. The second survey was fielded in March 2024. The results are in review as of July 2024. Iterative survey cycles will continue biannually until December 2026. Multidisciplinary dissemination of survey analysis results began in July 2023, and dissemination of secondary data findings is anticipated to begin January 2025. These processes are ongoing, with investigation to wrap up by June 2027. Conclusions: This study will detail appropriate and inappropriate antiepileptic drug use and related outcomes in NHs and describe disparities in long-stay subpopulations treated or not treated with psychotropics. It will delineate the impact of the pandemic in combination with national policies on dementia management and outcomes. We believe this mixed methods approach, including processes that link multiple CMS data sets at an individual level and survey-relevant stakeholders, can be replicated and applied to evaluate a variety of patient-oriented questions in diverse clinical populations. International Registered Report Identifier (IRRID): DERR1-10.2196/64446 ", doi="10.2196/64446", url="https://www.researchprotocols.org/2024/1/e64446" } @Article{info:doi/10.2196/44662, author="Slovis, Heritier Benjamin and Huang, Soonyip and McArthur, Melanie and Martino, Cara and Beers, Tasia and Labella, Meghan and Riggio, M. Jeffrey and Pribitkin, deAzevedo Edmund", title="Design and Implementation of an Opioid Scorecard for Hospital System--Wide Peer Comparison of Opioid Prescribing Habits: Observational Study", journal="JMIR Hum Factors", year="2024", month="Sep", day="9", volume="11", pages="e44662", keywords="opioids", keywords="peer comparison", keywords="quality", keywords="scorecard", keywords="prescribing", keywords="design", keywords="implementation", keywords="opioid", keywords="morbidity", keywords="mortality", keywords="opioid usage", keywords="opioid dependence", keywords="drug habits", abstract="Background: Reductions in opioid prescribing by health care providers can lead to a decreased risk of opioid dependence in patients. Peer comparison has been demonstrated to impact providers' prescribing habits, though its effect on opioid prescribing has predominantly been studied in the emergency department setting. Objective: The purpose of this study is to describe the development of an enterprise-wide opioid scorecard, the architecture of its implementation, and plans for future research on its effects. Methods: Using data generated by the author's enterprise vendor--based electronic health record, the enterprise analytics software, and expertise from a dedicated group of informaticists, physicians, and analysts, the authors developed an opioid scorecard that was released on a quarterly basis via email to all opioid prescribers at our institution. These scorecards compare providers' opioid prescribing habits on the basis of established metrics to those of their peers within their specialty throughout the enterprise. Results: At the time of this study's completion, 2034 providers have received at least 1 scorecard over a 5-quarter period ending in September 2021. Poisson regression demonstrated a 1.6\% quarterly reduction in opioid prescribing, and chi-square analysis demonstrated pre-post reductions in the proportion of prescriptions longer than 5 days' duration and a morphine equivalent daily dose of >50. Conclusions: To our knowledge, this is the first peer comparison effort with high-quality evidence-based metrics of this scale published in the literature. By sharing this process for designing the metrics and the process of distribution, the authors hope to influence other health systems to attempt to curb the opioid pandemic through peer comparison. Future research examining the effects of this intervention could demonstrate significant reductions in opioid prescribing, thus potentially reducing the progression of individual patients to opioid use disorder and the associated increased risk of morbidity and mortality. ", doi="10.2196/44662", url="https://humanfactors.jmir.org/2024/1/e44662" } @Article{info:doi/10.2196/63808, author="Kongkaew, Chuenjid and Phan, Anh Dang Thuc and Janusorn, Prathan and Mongkhon, Pajaree", title="Estimating Adverse Events Associated With Herbal Medicines Using Pharmacovigilance Databases: Systematic Review and Meta-Analysis", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="29", volume="10", pages="e63808", keywords="herbal medicine", keywords="pharmacovigilance", keywords="adverse event", keywords="spontaneous reporting system", keywords="meta-analysis", abstract="Background: Herbal medicines (HMs) are extensively used by consumers/patients worldwide. However, their safety profiles are often poorly reported and characterized. Previous studies have documented adverse events (AEs) associated with HMs, such as hepatotoxicity, renal failure, and allergic reactions. However, the prevalence rate of AEs related to HMs has been reported to be low. To date, no systematic review and meta-analysis has comprehensively analyzed the AEs of HMs using published data acquired from pharmacovigilance (PV) databases. Objective: This study aimed to (1) estimate the reporting rate of the AEs of HMs using PV databases and (2) assess the detailed data provided in AE reports. Methods: In this systematic review and meta-analysis, MEDLINE/PubMed, SCOPUS, EMBASE, and CINAHL were systematically searched for relevant studies (until December 2023). The DerSimonian-Laird random effects model was used for pooling the data, and subgroup analyses, the meta-regression model, and sensitivity analysis were used to explore the source of heterogeneity. Crombie's checklist was used to evaluate the risk of bias (ROB) of the included studies. Results: In total, 26 studies met the eligibility criteria. The reporting rate of the AEs of HMs ranged considerably, from 0.03\% to 29.84\%, with a median overall pooled estimate of 1.42\% (IQR 1.12\%-1.72\%). Subgroup analyses combined with the meta-regression model revealed that the reporting rate of the AEs of HMs was associated with the source of the reporter (P=.01). None of the included studies provided full details of suspected herbal products, only the main ingredients were disclosed, and other potentially harmful components were not listed. Conclusions: This systematic review and meta-analysis highlighted risks related to HMs, with a wide range of reporting rates, depending on the source of the reporter. Continuous efforts are necessary to standardize consumer reporting systems in terms of the reporting form, education, and follow-up strategy to improve data quality assurance, aiming to enhance the reliability and utility of PV data for monitoring the safety of HMs. Achieving effective monitoring and reporting of these AEs necessitates collaborative efforts from diverse stakeholders, including patients/consumers, manufacturers, physicians, complementary practitioners, sellers/distributors, and health authorities. Trial Registration: PROSPERO (Prospective International Register of Systematic Reviews) CRD42021276492 ", doi="10.2196/63808", url="https://publichealth.jmir.org/2024/1/e63808" } @Article{info:doi/10.2196/57885, author="Rao, K. Varun and Valdez, Danny and Muralidharan, Rasika and Agley, Jon and Eddens, S. Kate and Dendukuri, Aravind and Panth, Vandana and Parker, A. Maria", title="Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis", journal="J Med Internet Res", year="2024", month="Aug", day="23", volume="26", pages="e57885", keywords="digital epidemiology", keywords="BERTtopic", keywords="Valence Aware Dictionary and Sentiment Reasoner", keywords="VADER", keywords="sentiment analysis", keywords="social media", keywords="prescription drugs", keywords="prescription", keywords="prescriptions", keywords="drug", keywords="drugs", keywords="drug use", keywords="platform X", keywords="Twitter", keywords="tweet", keywords="tweets", keywords="latent Dirichlet allocation", keywords="machine-driven", keywords="natural language processing", keywords="NLP", keywords="brand name", keywords="logistic regression", keywords="machine learning", keywords="health informatics", abstract="Background: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing ``street names'' of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, ``brand name'' references were more amenable to machine-driven categorization. Objective: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. Methods: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency--inverse document frequency score. Results: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40\% compared with the models that did not incorporate the tweet text in both corpora. Conclusions: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non--drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet. ", doi="10.2196/57885", url="https://www.jmir.org/2024/1/e57885", url="http://www.ncbi.nlm.nih.gov/pubmed/39178036" } @Article{info:doi/10.2196/51317, author="Postma, J. Doerine and Heijkoop, A. Magali L. and De Smet, M. Peter A. G. and Notenboom, Kim and Leufkens, M. Hubert G. and Mantel-Teeuwisse, K. Aukje", title="Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study", journal="J Med Internet Res", year="2024", month="Aug", day="6", volume="26", pages="e51317", keywords="medicine shortages", keywords="signal detection", keywords="social media", keywords="Twitter social network", keywords="drug shortage", keywords="Twitter", abstract="Background: Early identification is critical for mitigating the impact of medicine shortages on patients. The internet, specifically social media, is an emerging source of health data. Objective: This study aimed to explore whether a routine analysis of data from the Twitter social network can detect signals of a medicine shortage and serve as an early warning system and, if so, for which medicines or patient groups. Methods: Medicine shortages between January 31 and December 1, 2019, were collected from the Dutch pharmacists' society's national catalog Royal Dutch Pharmacists Association (KNMP) Farmanco. Posts on these shortages were collected by searching for the name, the active pharmaceutical ingredient, or the first word of the brand name of the medicines in shortage. Posts were then selected based on relevant keywords that potentially indicated a shortage and the percentage of shortages with at least 1 post was calculated. The first posts per shortage were analyzed for their timing (median number of days, including the IQR) versus the national catalog, also stratified by disease and medicine characteristics. The content of the first post per shortage was analyzed descriptively for its reporting stakeholder and the nature of the post. Results: Of the 341 medicine shortages, 102 (29.9\%) were mentioned on Twitter. Of these 102 shortages, 18 (5.3\% of the total) were mentioned prior to or simultaneous to publication by KNMP Farmanco. Only 4 (1.2\%) of these were mentioned on Twitter more than 14 days before. On average, posts were published with a median delay of 37 (IQR 7-81) days to publication by KNMP Farmanco. Shortages mentioned on Twitter affected a greater number of patients and lasted longer than those that were not mentioned. We could not conclusively relate either the presence or absence on Twitter to a disease area or route of administration of the medicine in shortage. The first posts on the 102 shortages were mainly published by patients (n=51, 50.0\%) and health care professionals (n=46, 45.1\%). We identified 8 categories of nature of content. Sharing personal experience (n=44, 43.1\%) was the most common category. Conclusions: The Twitter social network is not a suitable early warning system for medicine shortages. Twitter primarily echoes already-known information rather than spreads new information. However, Twitter or potentially any other social media platform provides the opportunity for future qualitative research in the increasingly important field of medicine shortages that investigates how a larger population of patients is affected by shortages. ", doi="10.2196/51317", url="https://www.jmir.org/2024/1/e51317" } @Article{info:doi/10.2196/56277, author="Soerensen, Lykkegaard Ann and Haase Juhl, Marie and Krogh, Lunddal Marlene and Gr{\o}nkj{\ae}r, Mette and Kristensen, Kolding Jette and Olesen, Estrup Anne", title="Deprescribing as a Way to Reduce Inappropriate Use of Drugs for Overactive Bladder in Primary Care (DROP): Protocol for a Cluster Randomized Controlled Trial With an Embedded Explanatory Sequential Mixed Methods Study", journal="JMIR Res Protoc", year="2024", month="Jul", day="23", volume="13", pages="e56277", keywords="deprescribing", keywords="overactive bladder", keywords="general practice", keywords="patient safety", keywords="potentially inappropriate medication", keywords="geriatric", keywords="elderly", keywords="medication safety", keywords="geriatrics", keywords="anticholinergic drugs", keywords="safety", keywords="prescription", keywords="Denmark", keywords="general practitioner", keywords="evidence-based intervention", keywords="evidence-based", keywords="intervention", keywords="health care", keywords="medication", keywords="efficacy", keywords="DROP study", keywords="DROP", abstract="Background: Potentially inappropriate medication remains a significant concern in general practices, particularly in the context of overactive bladder (OAB) treatment for individuals aged 65 years and older. This study focuses on the exploration of alternative options for treating OAB and the deprescribing of anticholinergic drugs commonly used in OAB. The research aims to comprehensively evaluate the efficiency of deprescribing through a mixed methods approach, combining quantitative assessment and qualitative exploration of perceptions, experiences, and potential barriers among patients and health care personnel. Objective: This study aims to evaluate the efficiency and safety of the intervention in which health care staff in primary care encourage patients to participate in deprescribing their drugs for OAB. In addition, we aim to identify factors contributing to or obstructing the deprescribing process that will drive more informed decisions in the field of deprescribing and support effective and safe treatment of patients. Methods: The drugs for overactive bladder in primary care (DROP) study uses a rigorous research design, using a randomized controlled trial (RCT) with an embedded sequential explanatory mixed methods approach. All general practices within the North Denmark Region will be paired based on the number of general practitioners (GPs) and urban or rural locations. The matched pairs will be randomized into intervention and control groups. The intervention group will receive an algorithm designed to guide the deprescribing of drugs for OAB, promoting appropriate medication use. Quantitative data will be collected from the RCT including data from Danish registries for prescription analysis. Qualitative data will be obtained through interviews and focus groups with GPs, staff members, and patients. Finally, the quantitative and qualitative findings are merged to understand deprescribing for OAB comprehensively. This integrated approach enhances insights and supports future intervention improvement. Results: The DROP study is currently in progress, with randomization of general practices underway. While they have not been invited to participate yet, they will be. The inclusion of GP practices is scheduled from December 2023 to April 2024. The follow-up period for each patient is 6 months. Results will be analyzed through an intention-to-treat analysis for the RCT and a thematic analysis for the qualitative component. Quantitative outcomes will focus on changes in prescriptions and symptoms, while the qualitative analysis will explore experiences and perceptions. Conclusions: The DROP study aims to provide an evidence-based intervention in primary care that ensures the deprescription of drugs for OAB when there is an unfavorable risk-benefit profile. The DROP study's contribution lies in generating evidence for deprescribing practices and influencing best practices in health care. Trial Registration: ClinicalTrials.gov NCT06110975; https://clinicaltrials.gov/study/NCT06110975 International Registered Report Identifier (IRRID): DERR1-10.2196/56277 ", doi="10.2196/56277", url="https://www.researchprotocols.org/2024/1/e56277" } @Article{info:doi/10.2196/48156, author="Kale, U. Aditya and Dattani, Riya and Tabansi, Ashley and Hogg, Jeffry Henry David and Pearson, Russell and Glocker, Ben and Golder, Su and Waring, Justin and Liu, Xiaoxuan and Moore, J. David and Denniston, K. Alastair", title="AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review", journal="JMIR Res Protoc", year="2024", month="Jul", day="11", volume="13", pages="e48156", keywords="adverse event", keywords="artificial intelligence", keywords="regulatory science", keywords="regulatory database", keywords="safety issue", keywords="feedback", keywords="health care product", keywords="artificial intelligence health technology", keywords="reporting system", keywords="safety", keywords="medical devices", keywords="safety monitoring", keywords="risks", keywords="descriptive analysis", abstract="Background: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals. Objective: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance. Methods: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance. Results: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD. Conclusions: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes. International Registered Report Identifier (IRRID): PRR1-10.2196/48156 ", doi="10.2196/48156", url="https://www.researchprotocols.org/2024/1/e48156", url="http://www.ncbi.nlm.nih.gov/pubmed/38990628" } @Article{info:doi/10.2196/55663, author="Siefried, J. Krista and Bascombe, Florence and Clifford, Brendan and Liu, Zhixin and Middleton, Peter and Kay-Lambkin, Frances and Freestone, Jack and Herman, Daniel and Millard, Michael and Steele, Maureen and Acheson, Liam and Moller, Carl and Bath, Nicky and Ezard, Nadine", title="Effect of a Smartphone App (S-Check) on Actual and Intended Help-Seeking and Motivation to Change Methamphetamine Use Among Adult Consumers of Methamphetamine in Australia: Randomized Waitlist-Controlled Trial", journal="JMIR Mhealth Uhealth", year="2024", month="Jul", day="3", volume="12", pages="e55663", keywords="methamphetamine", keywords="smartphone app", keywords="behavior change", keywords="help-seeking", keywords="motivation to change", keywords="mHealth", keywords="mobile health", keywords="app", keywords="apps", keywords="application", keywords="applications", keywords="smartphone", keywords="smartphones", keywords="motivation", keywords="motivational", keywords="RCT", keywords="randomized", keywords="controlled trial", keywords="controlled trials", keywords="drug", keywords="drugs", keywords="substance use", keywords="engagement", keywords="substance abuse", keywords="mobile phone", abstract="Background: Interventions are required that address delays in treatment-seeking and low treatment coverage among people consuming methamphetamine. Objective: We aim to determine whether a self-administered smartphone-based intervention, the ``S-Check app'' can increase help-seeking and motivation to change methamphetamine use, and determine factors associated with app engagement. Methods: This study is a randomized, 28-day waitlist-controlled trial. Consenting adults residing in Australia who reported using methamphetamine at least once in the last month were eligible to download the app for free from Android or iOS app stores. Those randomized to the intervention group had immediate access to the S-Check app, the control group was wait-listed for 28 days before gaining access, and then all had access until day 56. Actual help-seeking and intention to seek help were assessed by the modified Actual Help Seeking Questionnaire (mAHSQ), modified General Help Seeking Questionnaire, and motivation to change methamphetamine use by the modified readiness ruler. $\chi$2 comparisons of the proportion of positive responses to the mAHSQ, modified General Help Seeking Questionnaire, and modified readiness ruler were conducted between the 2 groups. Logistic regression models compared the odds of actual help-seeking, intention to seek help, and motivation to change at day 28 between the 2 groups. Secondary outcomes were the most commonly accessed features of the app, methamphetamine use, feasibility and acceptability of the app, and associations between S-Check app engagement and participant demographic and methamphetamine use characteristics. Results: In total, 560 participants downloaded the app; 259 (46.3\%) completed eConsent and baseline; and 84 (32.4\%) provided data on day 28. Participants in the immediate access group were more likely to seek professional help (mAHSQ) at day 28 than those in the control group (n=15, 45.5\% vs n=12, 23.5\%; $\chi$21=4.42, P=.04). There was no significant difference in the odds of actual help-seeking, intention to seek help, or motivation to change methamphetamine use between the 2 groups on the primary logistic regression analyses, while in the ancillary analyses, the imputed data set showed a significant difference in the odds of seeking professional help between participants in the immediate access group compared to the waitlist control group (adjusted odds ratio 2.64, 95\% CI 1.19-5.83, P=.02). For participants not seeking help at baseline, each minute in the app increased the likelihood of seeking professional help by day 28 by 8\% (ratio 1.08, 95\% CI 1.02-1.22, P=.04). Among the intervention group, a 10-minute increase in app engagement time was associated with a decrease in days of methamphetamine use by 0.4 days (regression coefficient [$\beta$] --0.04, P=.02). Conclusions: The S-Check app is a feasible low-resource self-administered intervention for adults in Australia who consume methamphetamine. Study attrition was high and, while common in mobile health interventions, warrants larger studies of the S-Check app. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12619000534189; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377288\&isReview=true ", doi="10.2196/55663", url="https://mhealth.jmir.org/2024/1/e55663" } @Article{info:doi/10.2196/56755, author="Aronson, David Ian and Ardouin-Guerrier, Mary-Andr{\'e}e and Baus, Esteban Juan and Bennett, S. Alex", title="Barriers to, and Facilitators of, Checking Drugs for Adulterants in the Era of Fentanyl and Xylazine: Qualitative Study", journal="JMIR Form Res", year="2024", month="Jul", day="3", volume="8", pages="e56755", keywords="overdose", keywords="overdoses", keywords="fentanyl", keywords="xylazine", keywords="benzodiazepines", keywords="adulterants", keywords="drug", keywords="drugs", keywords="substance", keywords="substances", keywords="illicit drug", keywords="illicit drugs", keywords="drug test", keywords="drug testing", keywords="drug checking", keywords="qualitative", keywords="interview", keywords="interviews", keywords="digital health", keywords="digital technology", keywords="digital intervention", keywords="digital interventions", keywords="technological intervention", keywords="technological interventions", keywords="technology-based intervention", keywords="technology-based interventions", abstract="Background: Overdose deaths continue to reach new records in New York City and nationwide, largely driven by adulterants such as fentanyl and xylazine in the illicit drug supply. Unknowingly consuming adulterated substances dramatically increases risks of overdose and other health problems, especially when individuals consume multiple adulterants and are exposed to a combination of drugs they did not intend to take. Although test strips and more sophisticated devices enable people to check drugs for adulterants including fentanyl and xylazine prior to consumption and are often available free of charge, many people who use drugs decline to use them. Objective: We sought to better understand why people in the New York City area do or do not check drugs before use. We plan to use study findings to inform the development of technology-based interventions to encourage consistent drug checking. Methods: In summer 2023, team members who have experience working with people who use drugs conducted 22 semistructured qualitative interviews with a convenience sample of people who reported illicit drug use within the past 90 days. An interview guide examined participants' knowledge of and experience with adulterants including fentanyl, xylazine, and benzodiazepines; using drug testing strips; and whether they had ever received harm reduction services. All interviews were audio recorded, transcribed, and analyzed for emerging themes. Results: Most participants lacked knowledge of adulterants, and only a few reported regularly checking drugs. Reasons for not checking included lacking convenient access to test supplies, or a place to check samples out of the public's view, as well as time considerations. Some participants also reported a strong belief that they were not at risk from fentanyl, xylazine, or other adulterants because they exclusively used cocaine or crack, or that they were confident the people they bought drugs from would not sell them adulterated substances. Those who did report testing their drugs described positive interactions with harm reduction agency staff. Conclusions: New forms of outreach are needed not only to increase people's knowledge of adulterated substances and awareness of the increasing risks they pose but also to encourage people who use drugs to regularly check their substances prior to use. This includes new intervention messages that highlight the importance of drug checking in the context of a rapidly changing and volatile drug supply. This messaging can potentially help normalize drug checking as an easily enacted behavior that benefits public health. To increase effectiveness, messages can be developed with, and outreach can be conducted by, trusted community members including people who use drugs and, potentially, people who sell drugs. Pairing this messaging with access to no-cost drug-checking supplies and equipment may help address the ongoing spiral of increased overdose deaths nationwide. ", doi="10.2196/56755", url="https://formative.jmir.org/2024/1/e56755" } @Article{info:doi/10.2196/55228, author="Wallman, Andy and Sv{\"a}rdsudd, Kurt and Bobits, Kent and Wallman, Thorne", title="Antibiotic Prescribing by Digital Health Care Providers as Compared to Traditional Primary Health Care Providers: Cohort Study Using Register Data", journal="J Med Internet Res", year="2024", month="Jun", day="26", volume="26", pages="e55228", keywords="telehealth prescribing", keywords="physical-primary health care", keywords="internet-primary health care", keywords="antibiotics", keywords="prescription", keywords="infectious disease", keywords="antibiotic", keywords="prescriptions", keywords="prescribing", keywords="telehealth", keywords="health care", keywords="traditional", keywords="digital", keywords="telemedicine", keywords="virtual care", keywords="Swedish", keywords="Sweden", keywords="primary care", keywords="quality of care", keywords="online setting", keywords="ePrescription", keywords="ePrescriptions", keywords="ePrescribing", keywords="eHealth", keywords="compare", keywords="comparison", keywords="online consultation", keywords="digital care", keywords="patient record", keywords="patient records", keywords="mobile phone", abstract="Background: ?``Direct-to-consumer (DTC) telemedicine'' is increasing worldwide and changing the map of primary health care (PHC). Virtual care has increased in the last decade and with the ongoing COVID-19 pandemic, patients' use of online care has increased even further. In Sweden, online consultations are a part of government-supported health care today, and there are several digital care providers on the Swedish market, which makes it possible to get in touch with a doctor within a few minutes. The fast expansion of this market has raised questions about the quality of primary care provided only in an online setting without any physical appointments. Antibiotic prescribing is a common treatment in PHC. Objective: ?This study aimed to compare antibiotic prescribing between digital PHC providers (internet-PHC) and traditional physical PHC providers (physical-PHC) and to determine whether prescriptions for specific diagnoses differed between internet-PHC and physical-PHC appointments, adjusted for the effects of attained age at the time of appointment, gender, and time relative to the COVID-19 pandemic. Methods: ?Antibiotic prescribing data based on Anatomical Therapeutic Chemical (ATC) codes were obtained for Region S{\"o}rmland residents from January 2020 until March 2021 from the Regional Administrative Office. In total, 160,238 appointments for 68,332 S{\"o}rmland residents were included (124,398 physical-PHC and 35,840 internet-PHC appointments). Prescriptions issued by internet-PHC or physical-PHC physicians were considered. Information on the appointment date, staff category serving the patient, ICD-10 (International Statistical Classification of Diseases, Tenth Revision) diagnosis codes, ATC codes of prescribed medicines, and patient-attained age and gender were used. Results: ?A total of 160,238 health care appointments were registered, of which 18,433 led to an infection diagnosis. There were large differences in gender and attained age distributions among physical-PHC and internet-PHC appointments. Physical-PHC appointments peaked among patients aged 60-80 years while internet-PHC appointments peaked at 20-30 years of age for both genders. Antibiotics with the ATC codes J01A-J01X were prescribed in 9.3\% (11,609/124,398) of physical-PHC appointments as compared with 6.1\% (2201/35,840) of internet-PHC appointments. In addition, 61.3\% (6412/10,454) of physical-PHC infection appointments resulted in antibiotic prescriptions, as compared with only 25.8\% (2057/7979) of internet-PHC appointments. Analyses of the prescribed antibiotics showed that internet-PHC followed regional recommendations for all diagnoses. Physical-PHC also followed the recommendations but used a wider spectrum of antibiotics. The odds ratio of receiving an antibiotic prescription (after adjustments for attained age at the time of appointment, patient gender, and whether the prescription was issued before or during the COVID-19 pandemic) during an internet-PHC appointment was 0.23-0.39 as compared with a physical-PHC appointment. Conclusions: ?Internet-PHC appointments resulted in a significantly lower number of antibiotics prescriptions than physical-PHC appointments, adjusted for the large differences in the characteristics of patients who consult internet-PHC and physical-PHC. Internet-PHC prescribers showed appropriate prescribing according to guidelines. ", doi="10.2196/55228", url="https://www.jmir.org/2024/1/e55228" } @Article{info:doi/10.2196/46137, author="van Wyk, S. Susanna and Nliwasa, Marriott and Lu, Fang-Wen and Lan, Chih-Chan and Seddon, A. James and Hoddinott, Graeme and Viljoen, Lario and G{\"u}nther, Gunar and Ruswa, Nunurai and Shah, Sarita N. and Claassens, Mareli", title="Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review", journal="JMIR Public Health Surveill", year="2024", month="Jun", day="26", volume="10", pages="e46137", keywords="tuberculosis", keywords="drug-resistant tuberculosis", keywords="drug-resistant tuberculosis case finding", keywords="drug-resistant tuberculosis case detection", keywords="drug-resistant tuberculosis screening", keywords="drug-resistant tuberculosis contact investigation", keywords="scoping review", keywords="TB symptom", keywords="anti-tuberculosis drug", keywords="strategies", keywords="multidrug-resistant", keywords="systematic review", keywords="drug resistant", keywords="drug resistance", keywords="medication", keywords="diagnosis", keywords="screening", abstract="Background: Finding individuals with drug-resistant tuberculosis (DR-TB) is important to control the pandemic and improve patient clinical outcomes. To our knowledge, systematic reviews assessing the effectiveness, cost-effectiveness, acceptability, and feasibility of different DR-TB case-finding strategies to inform research, policy, and practice, have not been conducted and the scope of primary research is unknown. Objective: We therefore assessed the available literature on DR-TB case-finding strategies. Methods: We looked at systematic reviews, trials, qualitative studies, diagnostic test accuracy studies, and other primary research that sought to improve DR-TB case detection specifically. We excluded studies that included patients seeking care for tuberculosis (TB) symptoms, patients already diagnosed with TB, or were laboratory-based. We searched the academic databases of MEDLINE, Embase, The Cochrane Library, Africa-Wide Information, CINAHL (Cumulated Index to Nursing and Allied Health Literature), Epistemonikos, and PROSPERO (The International Prospective Register of Systematic Reviews) using no language or date restrictions. We screened titles, abstracts, and full-text articles in duplicate. Data extraction and analyses were carried out in Excel (Microsoft Corp). Results: We screened 3646 titles and abstracts and 236 full-text articles. We identified 6 systematic reviews and 61 primary studies. Five reviews described the yield of contact investigation and focused on household contacts, airline contacts, comparison between drug-susceptible tuberculosis and DR-TB contacts, and concordance of DR-TB profiles between index cases and contacts. One review compared universal versus selective drug resistance testing. Primary studies described (1) 34 contact investigations, (2) 17 outbreak investigations, (3) 3 airline contact investigations, (4) 5 epidemiological analyses, (5) 1 public-private partnership program, and (6) an e-registry program. Primary studies were all descriptive and included cross-sectional and retrospective reviews of program data. No trials were identified. Data extraction from contact investigations was difficult due to incomplete reporting of relevant information. Conclusions: Existing descriptive reviews can be updated, but there is a dearth of knowledge on the effectiveness, cost-effectiveness, acceptability, and feasibility of DR-TB case-finding strategies to inform policy and practice. There is also a need for standardization of terminology, design, and reporting of DR-TB case-finding studies. ", doi="10.2196/46137", url="https://publichealth.jmir.org/2024/1/e46137" } @Article{info:doi/10.2196/46176, author="Karapetiantz, Pierre and Audeh, Bissan and Redjdal, Akram and Tiffet, Th{\'e}ophile and Bousquet, C{\'e}dric and Jaulent, Marie-Christine", title="Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study", journal="J Med Internet Res", year="2024", month="Jun", day="18", volume="26", pages="e46176", keywords="pharmacovigilance", keywords="social media", keywords="scraper", keywords="natural language processing", keywords="signal detection", keywords="graphical user interface", abstract="Background: To mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios. Objective: The challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively. Methods: To enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority. Results: Between January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period. Conclusions: We demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events. ", doi="10.2196/46176", url="https://www.jmir.org/2024/1/e46176", url="http://www.ncbi.nlm.nih.gov/pubmed/38888956" } @Article{info:doi/10.2196/57239, author="Salvi, Amey and Gillenwater, A. Logan and Cockrum, P. Brandon and Wiehe, E. Sarah and Christian, Kaitlyn and Cayton, John and Bailey, Timothy and Schwartz, Katherine and Dir, L. Allyson and Ray, Bradley and Aalsma, C. Matthew and Reda, Khairi", title="Development of a Real-Time Dashboard for Overdose Touchpoints: User-Centered Design Approach", journal="JMIR Hum Factors", year="2024", month="Jun", day="11", volume="11", pages="e57239", keywords="overdose prevention", keywords="dashboards", keywords="fatality review", keywords="data integration", keywords="visualizations", keywords="visualization", keywords="dashboard", keywords="fatality", keywords="death", keywords="overdose", keywords="overdoses", keywords="overdosing", keywords="prevention", keywords="develop", keywords="development", keywords="design", keywords="interview", keywords="interviews", keywords="focus group", keywords="focus groups", keywords="touchpoints", keywords="touchpoint", keywords="substance abuse", keywords="drug abuse", abstract="Background: Overdose Fatality Review (OFR) is an important public health tool for shaping overdose prevention strategies in communities. However, OFR teams review only a few cases at a time, which typically represent a small fraction of the total fatalities in their jurisdiction. Such limited review could result in a partial understanding of local overdose patterns, leading to policy recommendations that do not fully address the broader community needs. Objective: This study explored the potential to enhance conventional OFRs with a data dashboard, incorporating visualizations of touchpoints---events that precede overdoses---to highlight prevention opportunities. Methods: We conducted 2 focus groups and a survey of OFR experts to characterize their information needs and design a real-time dashboard that tracks and measures decedents' past interactions with services in Indiana. Experts (N=27) were engaged, yielding insights on essential data features to incorporate and providing feedback to guide the development of visualizations. Results: The findings highlighted the importance of showing decedents' interactions with health services (emergency medical services) and the justice system (incarcerations). Emphasis was also placed on maintaining decedent anonymity, particularly in small communities, and the need for training OFR members in data interpretation. The developed dashboard summarizes key touchpoint metrics, including prevalence, interaction frequency, and time intervals between touchpoints and overdoses, with data viewable at the county and state levels. In an initial evaluation, the dashboard was well received for its comprehensive data coverage and its potential for enhancing OFR recommendations and case selection. Conclusions: The Indiana touchpoints dashboard is the first to display real-time visualizations that link administrative and overdose mortality data across the state. This resource equips local health officials and OFRs with timely, quantitative, and spatiotemporal insights into overdose risk factors in their communities, facilitating data-driven interventions and policy changes. However, fully integrating the dashboard into OFR practices will likely require training teams in data interpretation and decision-making. ", doi="10.2196/57239", url="https://humanfactors.jmir.org/2024/1/e57239", url="http://www.ncbi.nlm.nih.gov/pubmed/38861717" } @Article{info:doi/10.2196/50274, author="Ball, Robert and Talal, H. Andrew and Dang, Oanh and Mu{\~n}oz, Monica and Markatou, Marianthi", title="Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration", journal="J Med Internet Res", year="2024", month="Jun", day="6", volume="26", pages="e50274", keywords="drug safety", keywords="artificial intelligence", keywords="machine learning", keywords="natural language processing", keywords="causal inference", keywords="case-based reasoning", keywords="clinical decision support", doi="10.2196/50274", url="https://www.jmir.org/2024/1/e50274", url="http://www.ncbi.nlm.nih.gov/pubmed/38842929" } @Article{info:doi/10.2196/51323, author="Hopcroft, EM Lisa and Curtis, J. Helen and Croker, Richard and Pretis, Felix and Inglesby, Peter and Evans, David and Bacon, Sebastian and Goldacre, Ben and Walker, J. Alex and MacKenna, Brian", title="Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study", journal="JMIR Public Health Surveill", year="2024", month="Jun", day="5", volume="10", pages="e51323", keywords="electronic health records", keywords="primary care", keywords="general practice", keywords="opioid analgesics", keywords="data science", keywords="implementation science", keywords="data-driven", keywords="identification", keywords="intervention", keywords="implementations", keywords="proof of concept", keywords="opioid", keywords="unbiased", keywords="prescribing data", keywords="analysis tool", abstract="Background: We have previously demonstrated that opioid prescribing increased by 127\% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made. Objective: We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. Methods: We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures---total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate. Results: We identified a reduction in total opioid prescribing in 94 (49.2\%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40\% proportional drop (8.9\% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99\% drop in this same measure was found in several practices (44\%-95\% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. Conclusions: By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation. ", doi="10.2196/51323", url="https://publichealth.jmir.org/2024/1/e51323", url="http://www.ncbi.nlm.nih.gov/pubmed/38838327" } @Article{info:doi/10.2196/54428, author="Bittmann, A. Janina and Scherkl, Camilo and Meid, D. Andreas and Haefeli, E. Walter and Seidling, M. Hanna", title="Event Analysis for Automated Estimation of Absent and Persistent Medication Alerts: Novel Methodology", journal="JMIR Med Inform", year="2024", month="Jun", day="4", volume="12", pages="e54428", keywords="clinical decision support system", keywords="CDSS", keywords="medication alert system", keywords="alerting", keywords="alert acceptance", keywords="event analysis", abstract="Background: Event analysis is a promising approach to estimate the acceptance of medication alerts issued by computerized physician order entry (CPOE) systems with an integrated clinical decision support system (CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, which can be a time-consuming process, especially when performed manually. Objective: We present a new automated event analysis approach, which was applied to a large data set generated in a CPOE-CDSS with passive, noninterruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which the alert display was correlated with associated prescription changes. Alerts were considered ``persistent'' if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and were considered ``absent'' if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of 3 (IQR 1-7) consecutive 24-hour time intervals, with the shortest alerts displayed for drug-allergy interactions and the longest alerts displayed for potentially inappropriate medication for the elderly (PIM). Among the total 11,428 alerts, 56.1\% (n=6413) became absent, most commonly among alerts for drug-drug interactions (1915/2366, 80.9\%) and least commonly among PIM alerts (199/499, 39.9\%). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, noninterruptive alerts. This enables large data sets of longitudinal patient cases to be processed, allows for the derivation of the ratios of persistent and absent alerts, and facilitates the comparison and prospective monitoring of these alerts. ", doi="10.2196/54428", url="https://medinform.jmir.org/2024/1/e54428" } @Article{info:doi/10.2196/54023, author="Hakariya, Hayase and Yokoyama, Natsuki and Lee, Jeonse and Hakariya, Arisa and Ikejiri, Tatsuki", title="Illicit Trade of Prescription Medications Through X (Formerly Twitter) in Japan: Cross-Sectional Study", journal="JMIR Form Res", year="2024", month="May", day="28", volume="8", pages="e54023", keywords="illegal trading", keywords="pharmacovigilance", keywords="social networking service", keywords="SNS", keywords="overdose", keywords="social support", keywords="antipsychotics", keywords="Japan", keywords="prescription medication", keywords="cross-sectional study", keywords="prescription drug", keywords="social networking", keywords="medication", keywords="pharmaceutical", keywords="pharmaceutical drugs", keywords="Japanese", keywords="psychiatric", keywords="support", abstract="Background: Nonmedical use of prescription drugs can cause overdose; this represents a serious public health crisis globally. In this digital era, social networking services serve as viable platforms for illegal acquisition of excessive amounts of medications, including prescription medications. In Japan, such illegal drug transactions have been conducted through popular flea market applications, social media, and auction websites, with most of the trades being over-the-counter (OTC) medications. Recently, an emerging unique black market, where individuals trade prescription medications---predominantly nervous system drugs---using a specific keyword (``Okusuri Mogu Mogu''), has emerged on X (formerly Twitter). Hence, these dynamic methods of illicit trading should routinely be monitored to encourage the appropriate use of medications. Objective: This study aimed to specify the characteristics of medications traded on X using the search term ``Okusuri Mogu Mogu'' and analyze individual behaviors associated with X posts, including the types of medications traded and hashtag usage. Methods: We conducted a cross-sectional study with publicly available posts on X between September 18 and October 1, 2022. Posts that included the term ``Okusuri Mogu Mogu'' during this period were scrutinized. Posts were categorized on the basis of their contents: buying, selling, self-administration, heads-up, and others. Among posts categorized as buying, selling, and self-administration, medication names were systematically enumerated and categorized using the Anatomical Therapeutic Chemical (ATC) classification. Additionally, hashtags in all the analyzed posts were counted and classified into 6 categories: medication name, mental disorder, self-harm, buying and selling, community formation, and others. Results: Out of 961 identified posts, 549 were included for analysis. Of these posts, 119 (21.7\%) referenced self-administration, and 237 (43.2\%; buying: n=67, 12.2\%; selling: n=170, 31.0\%) referenced transactions. Among these 237 posts, 1041 medication names were mentioned, exhibiting a >5-fold increase from the study in March 2021. Categorization based on the ATC classification predominantly revealed nervous system drugs, representing 82.1\% (n=855) of the mentioned medications, consistent with the previous survey. Of note, the diversity of medications has expanded to include medications that have not been approved by the Japanese government. Interestingly, OTC medications were frequently mentioned in self-administration posts (odds ratio 23.6, 95\% CI 6.93-80.15). Analysis of hashtags (n=866) revealed efforts to foster community connections among users. Conclusions: This study highlighted the escalating complexity of trading of illegal prescription medication facilitated by X posts. Regulatory measures to enhance public awareness should be considered to prevent illegal transactions, which may ultimately lead to misuse or abuse such as overdose. Along with such pharmacovigilance measures, social approaches that could direct individuals to appropriate medical or psychiatric resources would also be beneficial as our hashtag analysis shed light on the formation of a cohesive or closed community among users. ", doi="10.2196/54023", url="https://formative.jmir.org/2024/1/e54023", url="http://www.ncbi.nlm.nih.gov/pubmed/38805262" } @Article{info:doi/10.2196/48572, author="Yue, Qi-Xuan and Ding, Ruo-Fan and Chen, Wei-Hao and Wu, Lv-Ying and Liu, Ke and Ji, Zhi-Liang", title="Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study", journal="J Med Internet Res", year="2024", month="May", day="3", volume="26", pages="e48572", keywords="clinical drug toxicity", keywords="adverse drug reaction", keywords="ADR severity", keywords="ADR frequency", keywords="mathematical model", abstract="Background: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. Objective: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. Methods: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity\_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. Results: The ADReCS severity-grading model exhibited excellent consistency (99.22\%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. Conclusions: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation. ", doi="10.2196/48572", url="https://www.jmir.org/2024/1/e48572", url="http://www.ncbi.nlm.nih.gov/pubmed/38700923" } @Article{info:doi/10.2196/52499, author="Leas, C. Eric and Ayers, W. John and Desai, Nimit and Dredze, Mark and Hogarth, Michael and Smith, M. Davey", title="Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection", journal="J Med Internet Res", year="2024", month="May", day="2", volume="26", pages="e52499", keywords="adverse events", keywords="artificial intelligence", keywords="AI", keywords="text analysis", keywords="annotation", keywords="ChatGPT", keywords="LLM", keywords="large language model", keywords="cannabis", keywords="delta-8-THC", keywords="delta-8-tetrahydrocannabiol", doi="10.2196/52499", url="https://www.jmir.org/2024/1/e52499", url="http://www.ncbi.nlm.nih.gov/pubmed/38696245" } @Article{info:doi/10.2196/54645, author="Asano, Masaki and Imai, Shungo and Shimizu, Yuri and Kizaki, Hayato and Ito, Yukiko and Tsuchiya, Makoto and Kuriyama, Ryoko and Yoshida, Nao and Shimada, Masanori and Sando, Takanori and Ishijima, Tomo and Hori, Satoko", title="Factor Analysis of Patients Who Find Tablets or Capsules Difficult to Swallow Due to Their Large Size: Using the Personal Health Record Infrastructure of Electronic Medication Notebooks", journal="J Med Internet Res", year="2024", month="Apr", day="24", volume="26", pages="e54645", keywords="tablet", keywords="tablets", keywords="capsules", keywords="capsule", keywords="size", keywords="personal health record", keywords="electronic medication notebook", keywords="patient preference", keywords="drug", keywords="drugs", keywords="pharmacy", keywords="pharmacies", keywords="pharmacology", keywords="pharmacotherapy", keywords="pharmaceutic", keywords="pharmaceutics", keywords="pharmaceuticals", keywords="pharmaceutical", keywords="medication", keywords="medications", keywords="preference", keywords="preferences", keywords="pill", keywords="pills", keywords="machine learning", keywords="decision tree", keywords="swallow", keywords="swallowing", keywords="throat", keywords="pharynx", keywords="risk", keywords="risks", keywords="dysphagia", keywords="speech", keywords="mobile phone", abstract="Background: Understanding patient preference regarding taking tablet or capsule formulations plays a pivotal role in treatment efficacy and adherence. Therefore, these preferences should be taken into account when designing formulations and prescriptions. Objective: This study investigates the factors affecting patient preference in patients who have difficulties swallowing large tablets or capsules and aims to identify appropriate sizes for tablets and capsules. Methods: A robust data set was developed based on a questionnaire survey conducted from December 1, 2022, to December 7, 2022, using the harmo smartphone app operated by harmo Co, Ltd. The data set included patient input regarding their tablet and capsule preferences, personal health records (including dispensing history), and drug formulation information (available from package inserts). Based on the medication formulation information, 6 indices were set for each of the tablets or capsules that were considered difficult to swallow owing to their large size and concomitant tablets or capsules (used as controls). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of each index. The index demonstrating the highest area under the curve of the ROC was selected as the best index to determine the tablet or capsule size that leads to swallowing difficulties. From the generated ROCs, the point with the highest discriminative performance that maximized the Youden index was identified, and the optimal threshold for each index was calculated. Multivariate logistic regression analysis was performed to identify the risk factors contributing to difficulty in swallowing oversized tablets or capsules. Additionally, decision tree analysis was performed to estimate the combined risk from several factors, using risk factors that were significant in the multivariate logistic regression analysis. Results: This study analyzed 147 large tablets or capsules and 624 control tablets or capsules. The ``long diameter + short diameter + thickness'' index (with a 21.5 mm threshold) was identified as the best indicator for causing swallowing difficulties in patients. The multivariate logistic regression analysis (including 132 patients with swallowing difficulties and 1283 patients without) results identified the following contributory risk factors: aged <50 years (odds ratio [OR] 1.59, 95\% CI 1.03-2.44), female (OR 2.54, 95\% CI 1.70-3.78), dysphagia (OR 3.54, 95\% CI 2.22-5.65), and taking large tablets or capsules (OR 9.74, 95\% CI 5.19-18.29). The decision tree analysis results suggested an elevated risk of swallowing difficulties for patients with taking large tablets or capsules. Conclusions: This study identified the most appropriate index and threshold for indicating that a given tablet or capsule size will cause swallowing difficulties, as well as the contributory risk factors. Although some sampling biases (eg, only including smartphone users) may exist, our results can guide the design of patient-friendly formulations and prescriptions, promoting better medication adherence. ", doi="10.2196/54645", url="https://www.jmir.org/2024/1/e54645", url="http://www.ncbi.nlm.nih.gov/pubmed/38657229" } @Article{info:doi/10.2196/55794, author="Nishioka, Satoshi and Watabe, Satoshi and Yanagisawa, Yuki and Sayama, Kyoko and Kizaki, Hayato and Imai, Shungo and Someya, Mitsuhiro and Taniguchi, Ryoo and Yada, Shuntaro and Aramaki, Eiji and Hori, Satoko", title="Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models", journal="J Med Internet Res", year="2024", month="Apr", day="16", volume="26", pages="e55794", keywords="cancer", keywords="anticancer drug", keywords="adverse event", keywords="side effect", keywords="patient-reported outcome", keywords="patients' voice", keywords="patient-oriented", keywords="patient narrative", keywords="natural language processing", keywords="deep learning", keywords="pharmaceutical care record", keywords="SOAP", abstract="Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80\% accuracy for both hand-foot syndrome (n=152, 91\%) and adverse events limiting patients' daily lives (n=157, 80.1\%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. ``Pain or numbness'' (n=57, 36.3\%), ``fever'' (n=46, 29.3\%), and ``nausea'' (n=40, 25.5\%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work. ", doi="10.2196/55794", url="https://www.jmir.org/2024/1/e55794", url="http://www.ncbi.nlm.nih.gov/pubmed/38625718" } @Article{info:doi/10.2196/53665, author="Hebard, Stephen and Weaver, GracieLee and Hansen, B. William and Ruppert, Scarlett", title="Evaluation of a Pilot Program to Prevent the Misuse of Prescribed Opioids Among Health Care Workers: Repeated Measures Survey Study", journal="JMIR Form Res", year="2024", month="Apr", day="12", volume="8", pages="e53665", keywords="health care workers", keywords="opioid misuse", keywords="pain management", keywords="prescription opioids", keywords="prevention", keywords="substance abuse", keywords="substance use", keywords="workers", abstract="Background: Overprescription of opioids has led to increased misuse of opioids, resulting in higher rates of overdose. The workplace can play a vital role in an individual's intentions to misuse prescription opioids with injured workers being prescribed opioids, at a rate 3 times the national average. For example, health care workers are at risk for injuries, opioid dispensing, and diversion. Intervening within a context that may contribute to risks for opioid misuse while targeting individual psychosocial factors may be a useful complement to interventions at policy and prescribing levels. Objective: This pilot study assessed the effects of a mobile-friendly opioid misuse intervention prototype tailored for health care workers using the preparation phase of a multiphase optimization strategy design. Methods: A total of 33 health care practitioners participated in the pilot intervention, which included 10 brief web-based lessons aimed at impacting psychosocial measures that underlie opioid misuse. The lesson topics included: addiction beliefs, addiction control, Centers for Disease Control and Prevention guidelines and recommendations, beliefs about patient-provider relationships and communication, control in communicating with providers, beliefs about self-monitoring pain and side effects, control in self-monitoring pain and side effects, diversion and disposal beliefs, diversion and disposal control, and a conclusion lesson. Using a treatment-only design, pretest and posttest surveys were collected. A general linear repeated measures ANOVA was used to assess mean differences from pretest to posttest. Descriptive statistics were used to assess participant feedback about the intervention. Results: After completing the intervention, participants showed significant mean changes with increases in knowledge of opioids (+0.459; P<.001), less favorable attitudes toward opioids (--1.081; P=.001), more positive beliefs about communication with providers (+0.205; P=.01), more positive beliefs about pain management control (+0.969; P<.001), and increased intentions to avoid opioid use (+0.212; P=.03). Of the 33 practitioners who completed the program, most felt positive about the information presented, and almost 70\% (23/33) agreed or strongly agreed that other workers in the industry should complete a program like this. Conclusions: While attempts to address the opioid crisis have been made through public health policies and prescribing initiatives, opioid misuse continues to rise. Certain industries place workers at greater risk for injury and opioid dispensing, making interventions that target workers in these industries of particular importance. Results from this pilot study show positive impacts on knowledge, attitudes, and beliefs about communicating with providers and pain management control, as well as intentions to avoid opioid misuse. However, the dropout rate and small sample size are severe limitations, and the results lack generalizability. Results will be used to inform program revisions and future optimization trials, with the intention of providing insight for future intervention development and evaluation of mobile-friendly eHealth interventions for employees. ", doi="10.2196/53665", url="https://formative.jmir.org/2024/1/e53665", url="http://www.ncbi.nlm.nih.gov/pubmed/38607664" } @Article{info:doi/10.2196/49751, author="Lyzwinski, Nathalie Lynnette and Elgendi, Mohamed and Menon, Carlo", title="Users' Acceptability and Perceived Efficacy of mHealth for Opioid Use Disorder: Scoping Review", journal="JMIR Mhealth Uhealth", year="2024", month="Apr", day="11", volume="12", pages="e49751", keywords="acceptability", keywords="addict", keywords="addiction", keywords="addictions", keywords="app", keywords="app-based", keywords="application", keywords="applications", keywords="apps", keywords="barrier", keywords="barriers", keywords="challenge", keywords="challenges", keywords="messaging", keywords="mHealth", keywords="mobile health", keywords="monitoring", keywords="opioid", keywords="opioids", keywords="overdose", keywords="overdosing", keywords="pharmacology", keywords="review methodology", keywords="review methods", keywords="scoping", keywords="sensor", keywords="sensors", keywords="SMS", keywords="substance abuse", keywords="substance use", keywords="text message", keywords="wearable technology", keywords="wearable", keywords="wearables", abstract="Background: The opioid crisis continues to pose significant challenges to global public health, necessitating the development of novel interventions to support individuals in managing their substance use and preventing overdose-related deaths. Mobile health (mHealth), as a promising platform for addressing opioid use disorder, requires a comprehensive understanding of user perspectives to minimize barriers to care and optimize the benefits of mHealth interventions. Objective: This study aims to synthesize qualitative insights into opioid users' acceptability and perceived efficacy of mHealth and wearable technologies for opioid use disorder. Methods: A scoping review of PubMed (MEDLINE) and Google Scholar databases was conducted to identify research on opioid user perspectives concerning mHealth-assisted interventions, including wearable sensors, SMS text messaging, and app-based technology. Results: Overall, users demonstrate a high willingness to engage with mHealth interventions to prevent overdose-related deaths and manage opioid use. Users perceive mHealth as an opportunity to access care and desire the involvement of trusted health care professionals in these technologies. User comfort with wearing opioid sensors emerged as a significant factor. Personally tailored content, social support, and encouragement are preferred by users. Privacy concerns and limited access to technology pose barriers to care. Conclusions: To maximize benefits and minimize risks for users, it is crucial to implement robust privacy measures, provide comprehensive user training, integrate behavior change techniques, offer professional and peer support, deliver tailored messages, incorporate behavior change theories, assess readiness for change, design stigma-reducing apps, use visual elements, and conduct user-focused research for effective opioid management in mHealth interventions. mHealth demonstrates considerable potential as a tool for addressing opioid use disorder and preventing overdose-related deaths, given the high acceptability and perceived benefits reported by users. ", doi="10.2196/49751", url="https://mhealth.jmir.org/2024/1/e49751", url="http://www.ncbi.nlm.nih.gov/pubmed/38602751" } @Article{info:doi/10.2196/49527, author="Hall, William Eric and Sullivan, Sean Patrick and Bradley, Heather", title="Estimated Number of Injection-Involved Overdose Deaths in US States From 2000 to 2020: Secondary Analysis of Surveillance Data", journal="JMIR Public Health Surveill", year="2024", month="Apr", day="5", volume="10", pages="e49527", keywords="death rate", keywords="death", keywords="drug abuse", keywords="drugs", keywords="injection drug use", keywords="injection", keywords="mortality", keywords="National Vital Statistics System", keywords="overdose death rate", keywords="overdose", keywords="state", keywords="substance abuse", keywords="Treatment Episode Dataset-Admission", keywords="treatment", abstract="Background: In the United States, both drug overdose mortality and injection-involved drug overdose mortality have increased nationally over the past 25 years. Despite documented geographic differences in overdose mortality and substances implicated in overdose mortality trends, injection-involved overdose mortality has not been summarized at a subnational level. Objective: We aimed to estimate the annual number of injection-involved overdose deaths in each US state from 2000 to 2020. Methods: We conducted a stratified analysis that used data from drug treatment admissions (Treatment Episodes Data Set--Admissions; TEDS-A) and the National Vital Statistics System (NVSS) to estimate state-specific percentages of reported drug overdose deaths that were injection-involved from 2000 to 2020. TEDS-A collects data on the route of administration and the type of substance used upon treatment admission. We used these data to calculate the percentage of reported injections for each drug type by demographic group (race or ethnicity, sex, and age group), year, and state. Additionally, using NVSS mortality data, the annual number of overdose deaths involving selected drug types was identified by the following specific multiple-cause-of-death codes: heroin or synthetic opioids other than methadone (T40.1, T40.4), natural or semisynthetic opioids and methadone (T40.2, T40.3), cocaine (T40.5), psychostimulants with abuse potential (T43.6), sedatives (T42.3, T42.4), and others (T36-T59.0). We used the probabilities of injection with the annual number of overdose deaths, by year, primary substance, and demographic groups to estimate the number of overdose deaths that were injection-involved. Results: In 2020, there were 91,071 overdose deaths among adults recorded in the United States, and 93.1\% (84,753/91,071) occurred in the 46 jurisdictions that reported data to TEDS-A. Slightly less than half (38,253/84,753, 45.1\%; 95\% CI 41.1\%-49.8\%) of those overdose deaths were estimated to be injection-involved, translating to 38,253 (95\% CI 34,839-42,181) injection-involved overdose deaths in 2020. There was large variation among states in the estimated injection-involved overdose death rate (median 14.72, range 5.45-31.77 per 100,000 people). The national injection-involved overdose death rate increased by 323\% (95\% CI 255\%-391\%) from 2010 (3.78, 95\% CI 3.33-4.31) to 2020 (15.97, 95\% CI 14.55-17.61). States in which the estimated injection-involved overdose death rate increased faster than the national average were disproportionately concentrated in the Northeast region. Conclusions: Although overdose mortality and injection-involved overdose mortality have increased dramatically across the country, these trends have been more pronounced in some regions. A better understanding of state-level trends in injection-involved mortality can inform the prioritization of public health strategies that aim to reduce overdose mortality and prevent downstream consequences of injection drug use. ", doi="10.2196/49527", url="https://publichealth.jmir.org/2024/1/e49527", url="http://www.ncbi.nlm.nih.gov/pubmed/38578676" } @Article{info:doi/10.2196/53086, author="Ashraf, Reza Amir and Mackey, Ken Tim and Fittler, Andr{\'a}s", title="Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online", journal="JMIR Public Health Surveill", year="2024", month="Mar", day="21", volume="10", pages="e53086", keywords="generative artificial intelligence", keywords="artificial intelligence", keywords="comparative assessment", keywords="search engines", keywords="online pharmacies", keywords="patient safety", keywords="generative", keywords="safety", keywords="search engine", keywords="search", keywords="searches", keywords="searching", keywords="website", keywords="websites", keywords="Google", keywords="Bing", keywords="retrieval", keywords="information seeking", keywords="illegal", keywords="pharmacy", keywords="pharmacies", keywords="risk", keywords="risks", keywords="consumer", keywords="consumers", keywords="customer", keywords="customers", keywords="recommendation", keywords="recommendations", keywords="vendor", keywords="vendors", keywords="substance use", keywords="substance abuse", keywords="controlled substances", keywords="controlled substance", keywords="drug", keywords="drugs", keywords="pharmaceutic", keywords="pharmaceutics", keywords="pharmaceuticals", keywords="pharmaceutical", keywords="medication", keywords="medications", abstract="Background: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. Objective: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. Methods: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Results: Of the 262 websites recommended in the AI-generated search results, 47.33\% (124/262) belonged to active online pharmacies, with 31.29\% (82/262) leading to legitimate ones. However, 19.04\% (24/126) of Bing Chat's and 13.23\% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24\%) compared to Google SGE (6/92, 6\%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27\%; P=.02) compared to Bing (3/40, 7\%). Conclusions: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations. ", doi="10.2196/53086", url="https://publichealth.jmir.org/2024/1/e53086", url="http://www.ncbi.nlm.nih.gov/pubmed/38512343" } @Article{info:doi/10.2196/44726, author="ElSherief, Mai and Sumner, Steven and Krishnasamy, Vikram and Jones, Christopher and Law, Royal and Kacha-Ochana, Akadia and Schieber, Lyna and De Choudhury, Munmun", title="Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study", journal="JMIR Form Res", year="2024", month="Feb", day="23", volume="8", pages="e44726", keywords="addiction treatment", keywords="machine learning", keywords="misinformation", keywords="natural language processing", keywords="opioid use disorder", keywords="social media", keywords="substance use", abstract="Background: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. Objective: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. Methods: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. Results: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8\%), the nature of addiction (68/303, 22.5\%), pharmacologic properties of substances (52/303, 16.9\%), injection drug use (36/303, 11.9\%), pain and opioids (28/303, 9.3\%), physical dependence of medications (22/303, 7.2\%), and tramadol use (7/303, 2.3\%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. Conclusions: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content. ", doi="10.2196/44726", url="https://formative.jmir.org/2024/1/e44726", url="http://www.ncbi.nlm.nih.gov/pubmed/38393772" } @Article{info:doi/10.2196/49755, author="Jeon, Min Soo and Lim, HyunJoo and Cheon, Hyo-bin and Ryu, Juhee and Kwon, Jin-Won", title="Assessing the Labeling Information on Drugs Associated With Suicide Risk: Systematic Review", journal="JMIR Public Health Surveill", year="2024", month="Jan", day="30", volume="10", pages="e49755", keywords="suicide", keywords="adverse drug events", keywords="review", keywords="drug", keywords="mental health", keywords="systematic review", keywords="drug induced suicide", keywords="drug reaction", keywords="substance use", keywords="suicidal", keywords="medication", keywords="suicide symptoms", keywords="suicidal risk", keywords="drugs", keywords="adverse drug event", abstract="Background: Drug-induced suicide (DIS) is a severe adverse drug reaction (ADR). Although clinical trials have provided evidence on DIS, limited investigations have been performed on rare ADRs, such as suicide. Objective: We aimed to systematically review case reports on DIS to provide evidence-based drug information. Methods: We searched PubMed to obtain case reports regarding DIS published until July 2021. Cases resulting from drugs that are no longer used or are nonapproved, substance use, and suicidal intentions were excluded. The quality of each case report was assessed using the CASE (Case Reports) checklist. We extracted data regarding demographics, medication history, suicide symptoms, and symptom improvement and evaluated the causality of DIS using the Naranjo score. Furthermore, to identify the potential suicidal risk of the unknown drugs, we compared the results of the causality assessment with those of the approved drug labels. Results: In 83 articles, we identified 152 cases involving 61 drugs. Antidepressants were reported as the most frequent causative drugs of DIS followed by immunostimulants. The causality assessment revealed 61 cases having possible, 89 cases having probable, and 2 cases having definite relationships with DIS. For approximately 85\% of suspected drugs, the risk of suicidal ADRs was indicated on the approved label; however, the approved labels for 9 drugs, including lumacaftor/ivacaftor, doxycycline, clozapine, dextromethorphan, adalimumab, infliximab, piroxicam, paclitaxel, and formoterol, did not provide information about these risks. Conclusions: We found several case reports involving drugs without suicide risk information on the drug label. Our findings might provide valuable insights into drugs that may cause suicidal ADRs. ", doi="10.2196/49755", url="https://publichealth.jmir.org/2024/1/e49755", url="http://www.ncbi.nlm.nih.gov/pubmed/38289650" } @Article{info:doi/10.2196/52495, author="Lau, Y. Erica and Cragg, Amber and Small, S. Serena and Butcher, Katherine and Hohl, M. Corinne", title="Characterizing and Comparing Adverse Drug Events Documented in 2 Spontaneous Reporting Systems in the Lower Mainland of British Columbia, Canada: Retrospective Observational Study", journal="JMIR Hum Factors", year="2024", month="Jan", day="18", volume="11", pages="e52495", keywords="adverse drug event reporting systems", keywords="side effect", keywords="side effects", keywords="drug", keywords="drugs", keywords="pharmacy", keywords="pharmacology", keywords="pharmacotherapy", keywords="pharmaceutic", keywords="pharmaceutics", keywords="pharmaceuticals", keywords="pharmaceutical", keywords="medication", keywords="medications", keywords="patient safety", keywords="health information technology", keywords="pharmacovigilance", keywords="adverse", keywords="safety", keywords="HIT", keywords="information system", keywords="information systems", keywords="reporting", keywords="descriptive statistics", keywords="monitoring", abstract="Background: Robust adverse drug event (ADE) reporting systems are crucial to monitor and identify drug safety signals, but the quantity and type of ADEs captured may vary by system characteristics. Objective: We compared ADEs reported in 2 different reporting systems in the same jurisdictions, the Patient Safety and Learning System--Adverse Drug Reaction (PSLS-ADR) and ActionADE, to understand report variation. Methods: This retrospective observational study analyzed reports entered into PSLS-ADR and ActionADE systems between December 1, 2019, and December 31, 2022. We conducted a comprehensive analysis including all events from both reporting systems to examine coverage and usage and understand the types of events captured in both systems. We calculated descriptive statistics for reporting facility type, patient demographics, serious events, and most reported drugs. We conducted a subanalysis focused on adverse drug reactions to enable direct comparisons between systems in terms of the volume and events reported. We stratified results by reporting system. Results: We performed the comprehensive analysis on 3248 ADE reports, of which 12.4\% (375/3035) were reported in PSLS-ADR and 87.6\% (2660/3035) were reported in ActionADE. Distribution of all events and serious events varied slightly between the 2 systems. Iohexol, gadobutrol, and empagliflozin were the most common culprit drugs (173/375, 46.2\%) in PSLS-ADR, while hydrochlorothiazide, apixaban, and ramipril (308/2660, 11.6\%) were common in ActionADE. We included 2728 reports in the subanalysis of adverse drug reactions, of which 12.9\% (353/2728) were reported in PSLS-ADR and 86.4\% (2357/2728) were reported in ActionADE. ActionADE captured 4- to 6-fold more comparable events than PSLS-ADR over this study's period. Conclusions: User-friendly and robust reporting systems are vital for pharmacovigilance and patient safety. This study highlights substantial differences in ADE data that were generated by different reporting systems. Understanding system factors that lead to varying reporting patterns can enhance ADE monitoring and should be taken into account when evaluating drug safety signals. ", doi="10.2196/52495", url="https://humanfactors.jmir.org/2024/1/e52495", url="http://www.ncbi.nlm.nih.gov/pubmed/38236629" } @Article{info:doi/10.2196/51921, author="Zheng, Yifan and Rowell, Brigid and Chen, Qiyuan and Kim, Yong Jin and Kontar, Al Raed and Yang, Jessie X. and Lester, A. Corey", title="Designing Human-Centered AI to Prevent Medication Dispensing Errors: Focus Group Study With Pharmacists", journal="JMIR Form Res", year="2023", month="Dec", day="25", volume="7", pages="e51921", keywords="artificial intelligence", keywords="communication", keywords="design methods", keywords="design", keywords="development", keywords="engineering", keywords="focus groups", keywords="human-computer interaction", keywords="medication errors", keywords="morbidity", keywords="mortality", keywords="patient safety", keywords="safety", keywords="SEIPS", keywords="Systems Engineering Initiative for Patient Safety", keywords="tool", keywords="user-centered design methods", keywords="user-centered", keywords="visualization", abstract="Background: Medication errors, including dispensing errors, represent a substantial worldwide health risk with significant implications in terms of morbidity, mortality, and financial costs. Although pharmacists use methods like barcode scanning and double-checking for dispensing verification, these measures exhibit limitations. The application of artificial intelligence (AI) in pharmacy verification emerges as a potential solution, offering precision, rapid data analysis, and the ability to recognize medications through computer vision. For AI to be embraced, it must be designed with the end user in mind, fostering trust, clear communication, and seamless collaboration between AI and pharmacists. Objective: This study aimed to gather pharmacists' feedback in a focus group setting to help inform the initial design of the user interface and iterative designs of the AI prototype. Methods: A multidisciplinary research team engaged pharmacists in a 3-stage process to develop a human-centered AI system for medication dispensing verification. To design the AI model, we used a Bayesian neural network that predicts the dispensed pills' National Drug Code (NDC). Discussion scripts regarding how to design the system and feedback in focus groups were collected through audio recordings and professionally transcribed, followed by a content analysis guided by the Systems Engineering Initiative for Patient Safety and Human-Machine Teaming theoretical frameworks. Results: A total of 8 pharmacists participated in 3 rounds of focus groups to identify current challenges in medication dispensing verification, brainstorm solutions, and provide feedback on our AI prototype. Participants considered several teaming scenarios, generally favoring a hybrid teaming model where the AI assists in the verification process and a pharmacist intervenes based on medication risk level and the AI's confidence level. Pharmacists highlighted the need for improving the interpretability of AI systems, such as adding stepwise checkmarks, probability scores, and details about drugs the AI model frequently confuses with the target drug. Pharmacists emphasized the need for simplicity and accessibility. They favored displaying only essential information to prevent overwhelming users with excessive data. Specific design features, such as juxtaposing pill images with their packaging for quick comparisons, were requested. Pharmacists preferred accept, reject, or unsure options. The final prototype interface included (1) checkmarks to compare pill characteristics between the AI-predicted NDC and the prescription's expected NDC, (2) a histogram showing predicted probabilities for the AI-identified NDC, (3) an image of an AI-provided ``confused'' pill, and (4) an NDC match status (ie, match, unmatched, or unsure). Conclusions: In partnership with pharmacists, we developed a human-centered AI prototype designed to enhance AI interpretability and foster trust. This initiative emphasized human-machine collaboration and positioned AI as an augmentative tool rather than a replacement. This study highlights the process of designing a human-centered AI for dispensing verification, emphasizing its interpretability, confidence visualization, and collaborative human-machine teaming styles. ", doi="10.2196/51921", url="https://formative.jmir.org/2023/1/e51921", url="http://www.ncbi.nlm.nih.gov/pubmed/38145475" } @Article{info:doi/10.2196/50110, author="Ben-Aharon, Irit and Rotem, Ran and Melzer-Cohen, Cheli and Twig, Gilad and Cercek, Andrea and Half, Elizabeth and Goshen-Lago, Tal and Chodik, Gabriel and Kelsen, David", title="Pharmaceutical Agents as Potential Drivers in the Development of Early-Onset Colorectal Cancer: Case-Control Study", journal="JMIR Public Health Surveill", year="2023", month="Dec", day="13", volume="9", pages="e50110", keywords="early onset colorectal cancer", keywords="pharmaceutical agents", keywords="increased risk", keywords="colorectal cancer", keywords="health provider", keywords="Israel", keywords="machine learning", keywords="decision tree", keywords="gradient boosting", keywords="risk factors", keywords="decision support", keywords="risk", keywords="risks", keywords="colorectal", keywords="cancer", keywords="oncology", keywords="internal medicine", keywords="gastroenterology", keywords="gastrointestinal", keywords="pharmaceutical", keywords="pharmaceuticals", keywords="drug", keywords="drugs", abstract="Background: The incidence of early-onset colorectal cancer (EOCRC) rose abruptly in the mid 1990s, is continuing to increase, and has now been noted in many countries. By 2030, 25\% of American patients diagnosed with rectal cancer will be 49 years or younger. The large majority of EOCRC cases are not found in patients with germline cancer susceptibility mutations (eg, Lynch syndrome) or inflammatory bowel disease. Thus, environmental or lifestyle factors are suspected drivers. Obesity, sedentary lifestyle, diabetes mellitus, smoking, alcohol, or antibiotics affecting the gut microbiome have been proposed. However, these factors, which have been present since the 1950s, have not yet been conclusively linked to the abrupt increase in EOCRC. The sharp increase suggests the introduction of a new risk factor for young people. We hypothesized that the driver may be an off-target effect of a pharmaceutical agent (ie, one requiring regulatory approval before its use in the general population or an off-label use of a previously approved agent) in a genetically susceptible subgroup of young adults. If a pharmaceutical agent is an EOCRC driving factor, regulatory risk mitigation strategies could be used. Objective: We aimed to evaluate the possibility that pharmaceutical agents serve as risk factors for EOCRC. Methods: We conducted a case-control study. Data including demographics, comorbidities, and complete medication dispensing history were obtained from the electronic medical records database of Maccabi Healthcare Services, a state-mandated health provider covering 26\% of the Israeli population. The participants included 941 patients with EOCRC (?50 years of age) diagnosed during 2001-2019 who were density matched at a ratio of 1:10 with 9410 control patients. Patients with inflammatory bowel disease and those with a known inherited cancer susceptibility syndrome were excluded. An advanced machine learning algorithm based on gradient boosted decision trees coupled with Bayesian model optimization and repeated data sampling was used to sort through the very high-dimensional drug dispensing data to identify specific medication groups that were consistently linked with EOCRC while allowing for synergistic or antagonistic interactions between medications. Odds ratios for the identified medication classes were obtained from a conditional logistic regression model. Results: Out of more than 800 medication classes, we identified several classes that were consistently associated with EOCRC risk across independently trained models. Interactions between medication groups did not seem to substantially affect the risk. In our analysis, drug groups that were consistently positively associated with EOCRC included beta blockers and valerian (Valeriana officinalis). Antibiotics were not consistently associated with EOCRC risk. Conclusions: Our analysis suggests that the development of EOCRC may be correlated with prior use of specific medications. Additional analyses should be used to validate the results. The mechanism of action inducing EOCRC by candidate pharmaceutical agents will then need to be determined. ", doi="10.2196/50110", url="https://publichealth.jmir.org/2023/1/e50110", url="http://www.ncbi.nlm.nih.gov/pubmed/37933755" } @Article{info:doi/10.2196/45021, author="Kariya, Azusa and Okada, Hiroshi and Suzuki, Shota and Dote, Satoshi and Nishikawa, Yoshitaka and Araki, Kazuo and Takahashi, Yoshimitsu and Nakayama, Takeo", title="Internet-Based Inquiries From Users With the Intention to Overdose With Over-the-Counter Drugs: Qualitative Analysis of Yahoo! Chiebukuro", journal="JMIR Form Res", year="2023", month="Nov", day="22", volume="7", pages="e45021", keywords="abuse", keywords="consumer-generated media", keywords="CGM", keywords="overdose", keywords="over-the-counter drug", keywords="OTC drug", keywords="question and answer site", keywords="Q and A site", abstract="Background: Public concern with regard to over-the-counter (OTC) drug abuse is growing rapidly across countries. OTC drug abuse has serious effects on the mind and body, such as poisoning symptoms, and often requires specialized treatments. In contrast, there is concern about people who potentially abuse OTC drugs whose symptoms are not serious enough to consult medical institutions or drug addiction rehabilitation centers yet are at high risk of becoming drug dependent in the future. Objective: Consumer-generated media (CGM), which allows users to disseminate information, is being used by people who abuse (and those who are trying to abuse) OTC drugs to obtain information about OTC drug abuse. This study aims to analyze the content of CGM to explore the questions of people who potentially abuse OTC drugs. Methods: The subject of this research was Yahoo! Chiebukuro, the largest question and answer website in Japan. A search was performed using the names of drugs commonly used in OTC drug abuse and the keywords overdose and OD, and the number of questions posted on the content of OTC drug abuse was counted. Furthermore, a thematic analysis was conducted by extracting text data on the most abused antitussive and expectorant drug, BRON. Results: The number of questions about the content of overdose medications containing the keyword BRON has increased sharply as compared with other product names. Furthermore, 467 items of question data that met the eligibility criteria were obtained from 528 items of text data on BRON; 26 codes, 6 categories, and 3 themes were generated from the 578 questions contained in these items. Questions were asked about the effects they would gain from abusing OTC drugs and the information they needed to obtain the effects they sought, as well as about the effects of abuse on their bodies. Moreover, there were questions on how to stop abusing and what is needed when seeking help from a health care provider if they become dependent. It has become clear that people who abuse OTC drugs have difficulty in consulting face-to-face with others, and CGM is used as a means to obtain the necessary information anonymously. Conclusions: On CGM, people who abused or tried to abuse OTC drugs were asking questions about their abuse expectations and anxieties. In addition, when they became dependent, they sought advice to quit their abuse. CGM was used to exchange information about OTC drug abuse, and many questions on anxieties and hesitations were posted. This study suggests that it is necessary to produce and disseminate information on OTC drug abuse, considering the situation of those who abuse or are willing to abuse OTC drugs. Support from pharmacies and drugstores would also be essential to reduce opportunities for OTC drug abuse. ", doi="10.2196/45021", url="https://formative.jmir.org/2023/1/e45021", url="http://www.ncbi.nlm.nih.gov/pubmed/37991829" } @Article{info:doi/10.2196/45660, author="Carabot, Federico and Donat-Vargas, Carolina and Santoma-Vilaclara, Javier and Ortega, A. Miguel and Garc{\'i}a-Montero, Cielo and Fraile-Mart{\'i}nez, Oscar and Zaragoza, Cristina and Monserrat, Jorge and Alvarez-Mon, Melchor and Alvarez-Mon, Angel Miguel", title="Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study", journal="J Med Internet Res", year="2023", month="Nov", day="14", volume="25", pages="e45660", keywords="awareness", keywords="codeine", keywords="machine learning", keywords="pain", keywords="painkiller", keywords="perception", keywords="recreational use", keywords="social media", keywords="twitter", abstract="Background: Paracetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction. Objective: This study aims to explore the perceptions and experiences of Twitter users concerning these drugs. Methods: We analyzed the tweets in English or Spanish mentioning paracetamol, tramadol, or codeine posted between January 2019 and December 2020. Out of 152,056 tweets collected, 49,462 were excluded. The content was categorized using a codebook, distinguishing user types (patients, health care professionals, and institutions), and classifying medical content based on efficacy and adverse effects. Scientific accuracy and nonmedical content themes (commercial, economic, solidarity, and trivialization) were also assessed. A total of 1000 tweets for each drug were manually classified to train, test, and validate machine learning classifiers. Results: Of classifiable tweets, 42,840 mentioned paracetamol and 42,131 mentioned weak opioids (tramadol or codeine). Patients accounted for 73.10\% (60,771/83,129) of the tweets, while health care professionals and institutions received the highest like-tweet and tweet-retweet ratios. Medical content distribution significantly differed for each drug (P<.001). Nonmedical content dominated opioid tweets (23,871/32,307, 73.9\%), while paracetamol tweets had a higher prevalence of medical content (33,943/50,822, 66.8\%). Among medical content tweets, 80.8\% (41,080/50,822) mentioned drug efficacy, with only 6.9\% (3501/50,822) describing good or sufficient efficacy. Nonmedical content distribution also varied significantly among the different drugs (P<.001). Conclusions: Patients seeking relief from pain are highly interested in the effectiveness of drugs rather than potential side effects. Alarming trends include a significant number of tweets trivializing drug use and recreational purposes, along with a lack of awareness regarding side effects. Monitoring conversations related to analgesics on social media is essential due to common illegal web-based sales and purchases without prescriptions. ", doi="10.2196/45660", url="https://www.jmir.org/2023/1/e45660", url="http://www.ncbi.nlm.nih.gov/pubmed/37962927" } @Article{info:doi/10.2196/50013, author="Carabot, Federico and Fraile-Mart{\'i}nez, Oscar and Donat-Vargas, Carolina and Santoma, Javier and Garcia-Montero, Cielo and Pinto da Costa, Mariana and Molina-Ruiz, M. Rosa and Ortega, A. Miguel and Alvarez-Mon, Melchor and Alvarez-Mon, Angel Miguel", title="Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study", journal="J Med Internet Res", year="2023", month="Oct", day="31", volume="25", pages="e50013", keywords="awareness", keywords="epidemic", keywords="fentanyl", keywords="health communication", keywords="infodemiology", keywords="machine learning", keywords="opioids", keywords="recreational use", keywords="social media listening", keywords="Twitter", keywords="user", abstract="Background: Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance. Objective: The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition. Methods: In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27\% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug's efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug. Results: Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27\% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70\% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2\% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60\% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90\% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery. Conclusions: The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use. ", doi="10.2196/50013", url="https://www.jmir.org/2023/1/e50013", url="http://www.ncbi.nlm.nih.gov/pubmed/37906234" } @Article{info:doi/10.2196/49025, author="Ranusch, Allison and Lin, Ying-Jen and Dorsch, P. Michael and Allen, L. Arthur and Spoutz, Patrick and Seagull, Jacob F. and Sussman, B. Jeremy and Barnes, D. Geoffrey", title="Role of Individual Clinician Authority in the Implementation of Informatics Tools for Population-Based Medication Management: Qualitative Semistructured Interview Study", journal="JMIR Hum Factors", year="2023", month="Oct", day="24", volume="10", pages="e49025", keywords="direct oral anticoagulant", keywords="population management", keywords="implementation science", keywords="medical informatics", keywords="individual clinician authority", keywords="electronic health record", keywords="health records", keywords="EHR", keywords="EHRs", keywords="implementation", keywords="clotting", keywords="clot", keywords="clots", keywords="anticoagulant", keywords="anticoagulants", keywords="dashboard", keywords="DOAC", keywords="satisfaction", keywords="interview", keywords="interviews", keywords="pharmacist", keywords="pharmacy", keywords="pharmacology", keywords="medication", keywords="prescribe", keywords="prescribing", abstract="Background: Direct oral anticoagulant (DOAC) medications are frequently associated with inappropriate prescribing and adverse events. To improve the safe use of DOACs, health systems are implementing population health tools within their electronic health record (EHR). While EHR informatics tools can help increase awareness of inappropriate prescribing of medications, a lack of empowerment (or insufficient empowerment) of nonphysicians to implement change is a key barrier. Objective: This study examined how the individual authority of clinical pharmacists and anticoagulation nurses is impacted by and changes the implementation success of an EHR DOAC Dashboard for safe DOAC medication prescribing. Methods: We conducted semistructured interviews with pharmacists and nurses following the implementation of the EHR DOAC Dashboard at 3 clinical sites. Interview transcripts were coded according to the key determinants of implementation success. The intersections between individual clinician authority and other determinants were examined to identify themes. Results: A high level of individual clinician authority was associated with high levels of key facilitators for effective use of the DOAC Dashboard (communication, staffing and work schedule, job satisfaction, and EHR integration). Conversely, a lack of individual authority was often associated with key barriers to effective DOAC Dashboard use. Positive individual authority was sometimes present with a negative example of another determinant, but no evidence was found of individual authority co-occurring with a positive instance of another determinant. Conclusions: Increased individual clinician authority is a necessary antecedent to the effective implementation of an EHR DOAC Population Management Dashboard and positively affects other aspects of implementation. International Registered Report Identifier (IRRID): RR2-10.1186/s13012-020-01044-5 ", doi="10.2196/49025", url="https://humanfactors.jmir.org/2023/1/e49025", url="http://www.ncbi.nlm.nih.gov/pubmed/37874636" } @Article{info:doi/10.2196/51754, author="Oreskovic, Jessica and Kaufman, Jaycee and Thommandram, Anirudh and Fossat, Yan", title="A Radar-Based Opioid Overdose Detection Device for Public Restrooms: Design, Development, and Evaluation Study", journal="JMIR Biomed Eng", year="2023", month="Oct", day="24", volume="8", pages="e51754", keywords="60 GHz radar", keywords="opioid overdose", keywords="overdose detection", keywords="overdose prevention", keywords="respiratory depression", abstract="Background: The opioid epidemic is a growing crisis worldwide. While many interventions have been put in place to try to protect people from opioid overdoses, they typically rely on the person to take initiative in protecting themselves, requiring forethought, preparation, and action. Respiratory depression or arrest is the mechanism by which opioid overdoses become fatal, but it can be reversed with the timely administration of naloxone. Objective: In this study, we described the development and validation of an opioid overdose detection radar (ODR), specifically designed for use in public restroom stalls. In-laboratory testing was conducted to validate the noncontact, privacy-preserving device against a respiration belt and to determine the accuracy and reliability of the device. Methods: We used an ODR system with a high-frequency pulsed coherent radar sensor and a Raspberry Pi (Raspberry Pi Ltd), combining advanced technology with a compact and cost-effective setup to monitor respiration and detect opioid overdoses. To determine the optimal position for the ODR within the confined space of a restroom stall, iterative testing was conducted, considering the radar's bounded capture area and the limitations imposed by the stall's dimensions and layout. By adjusting the orientation of the ODR, we were able to identify the most effective placement where the device reliably tracked respiration in a number of expected positions. Experiments used a mock restroom stall setup that adhered to building code regulations, creating a controlled environment while maintaining the authenticity of a public restroom stall. By simulating different body positions commonly associated with opioid overdoses, the ODR's ability to accurately track respiration in various scenarios was assessed. To determine the accuracy of the ODR, testing was performed using a respiration belt as a reference. The radar measurements were compared with those obtained from the belt in experiments where participants were seated upright and slumped over. Results: The results demonstrated favorable agreement between the radar and belt measurements, with an overall mean error in respiration cycle duration of 0.0072 (SD 0.54) seconds for all recorded respiration cycles (N=204). During the simulated overdose experiments where participants were slumped over, the ODR successfully tracked respiration with a mean period difference of 0.0091 (SD 0.62) seconds compared with the reference data. Conclusions: The findings suggest that the ODR has the potential to detect significant deviations in respiration patterns that may indicate an opioid overdose event. The success of the ODR in these experiments indicates the device should be further developed and implemented to enhance safety and emergency response measures in public restrooms. However, additional validation is required for unhealthy opioid-influenced respiratory patterns to guarantee the ODR's effectiveness in real-world overdose situations. ", doi="10.2196/51754", url="https://biomedeng.jmir.org/2023/1/e51754", url="http://www.ncbi.nlm.nih.gov/pubmed/38875668" } @Article{info:doi/10.2196/45225, author="Lou, Pei and Fang, An and Zhao, Wanqing and Yao, Kuanda and Yang, Yusheng and Hu, Jiahui", title="Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph--Based Approach", journal="J Med Internet Res", year="2023", month="Oct", day="20", volume="25", pages="e45225", keywords="coronavirus", keywords="heterogeneous data integration", keywords="knowledge graph embedding", keywords="drug repurposing", keywords="interpretable prediction", keywords="COVID-19", abstract="Background: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. Objective: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph--based approach. Methods: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. Results: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. Conclusions: We showed the effectiveness of a knowledge graph--based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications. ", doi="10.2196/45225", url="https://www.jmir.org/2023/1/e45225", url="http://www.ncbi.nlm.nih.gov/pubmed/37862061" } @Article{info:doi/10.2196/48386, author="da Silva Lopes, Manuel Andr{\'e} and Colomer-Lahiguera, Sara and Darnac, C{\'e}lia and Giacomini, Stellio and Bugeia, S{\'e}bastien and Gutknecht, Garance and Spurrier-Bernard, Gilliosa and Cuendet, Michel and Muet, Fanny and Aedo-Lopez, Veronica and Mederos, Nuria and Michielin, Olivier and Addeo, Alfredo and Latifyan, Sofiya and Eicher, Manuela", title="Testing a Model of Care for Patients on Immune Checkpoint Inhibitors Based on Electronic Patient-Reported Outcomes: Protocol for a Randomized Phase II Controlled Trial", journal="JMIR Res Protoc", year="2023", month="Oct", day="18", volume="12", pages="e48386", keywords="patient-reported outcomes", keywords="model of care", keywords="immune-related adverse events", keywords="remote symptom management", keywords="self-management support", keywords="self-efficacy", keywords="health-related quality of life", keywords="eHealth", keywords="telehealth", keywords="support", keywords="self-management", keywords="symptom", keywords="monitoring", keywords="cancer", keywords="electronic health record", keywords="immune", keywords="detection", keywords="questionnaire", keywords="treatment", abstract="Background: Management of severe symptomatic immune-related adverse events (IrAEs) related to immune checkpoint inhibitors (ICIs) can be facilitated by timely detection. As patients face a heterogeneous set of symptoms outside the clinical setting, remotely monitoring and assessing symptoms by using patient-reported outcomes (PROs) may result in shorter delays between symptom onset and clinician detection. Objective: We assess the effect of a model of care for remote patient monitoring and symptom management based on PRO data on the time to detection of symptomatic IrAEs from symptom onset. The secondary objectives are to assess its effects on the time between symptomatic IrAE detection and intervention, IrAE grade (severity), health-related quality of life, self-efficacy, and overall survival at 6 months. Methods: For this study, 198 patients with cancer receiving systemic treatment comprising ICIs exclusively will be recruited from 2 Swiss university hospitals. Patients are randomized (1:1) to a digital model of care (intervention) or usual care (control group). Patients are enrolled for 6 months, and they use an electronic app to complete weekly Functional Assessment of Cancer Therapy-General questionnaire and PROMIS (PROs Measurement Information System) Self-Efficacy to Manage Symptoms questionnaires. The intervention patient group completes a standard set of 37 items in a weekly PROs version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) questionnaire, and active symptoms are reassessed daily for the first 3 months by using a modified 24-hour recall period. Patients can add items from the full PRO-CTCAE item library to their questionnaire. Nurses call patients in the event of new or worsening symptoms and manage them by using a standardized triage algorithm based on the United Kingdom Oncology Nursing Society 24-hour triage tool. This algorithm provides guidance on deciding if patients should receive in-person care, if monitoring should be increased, or if self-management education should be reinforced. Results: The Institut Suisse de Recherche Exp{\'e}rimentale sur le Cancer Foundation and Kaiku Health Ltd funded this study. Active recruitment began since November 2021 and is projected to conclude in November 2023. Trial results are expected to be published in the first quarter of 2024 and will be disseminated through publications submitted at international scientific conferences. Conclusions: This trial is among the first trials to use PRO data to directly influence routine care of patients treated with ICIs and addresses some limitations in previous studies. This trial collects a wider spectrum of self-reported symptom data daily. There are some methodological limitations brought by changes in evolving treatment standards for patients with cancer. This trial's results could entail further academic discussions on the challenges of diagnosing and managing symptoms associated with treatment remotely by providing further insights into the burden symptoms represent to patients and highlight the complexity of care procedures involved in managing symptomatic IrAEs. Trial Registration: ClinicalTrials.gov NCT05530187; https://www.clinicaltrials.gov/study/NCT05530187 International Registered Report Identifier (IRRID): DERR1-10.2196/48386 ", doi="10.2196/48386", url="https://www.researchprotocols.org/2023/1/e48386", url="http://www.ncbi.nlm.nih.gov/pubmed/37851498" } @Article{info:doi/10.2196/51825, author="Xia, Ting and Picco, Louisa and Lalic, Samanta and Buchbinder, Rachelle and Bell, Simon J. and Andrew, E. Nadine and Lubman, I. Dan and Pearce, Christopher and Nielsen, Suzanne", title="Determining the Impact of Opioid Policy on Substance Use and Mental Health--Related Harms: Protocol for a Data Linkage Study", journal="JMIR Res Protoc", year="2023", month="Oct", day="17", volume="12", pages="e51825", keywords="data linkage", keywords="drug policy", keywords="general practice", keywords="opioid", keywords="primary care", abstract="Background: Increasing harms related to prescription opioids over the past decade have led to the introduction of a range of key national and state policy initiatives across Australia. These include introducing a mandatory real-time prescription drug--monitoring program in the state of Victoria from April 2020 and a series of changes to subsidies for opioids on the Pharmaceutical Benefit Scheme from June 2020. Together, these changes aim to influence opioid supply and reduce harms related to prescription opioids, yet few studies have specifically explored how these policies have influenced opioid prescribing and related harms in Australia. Objective: The aim of this study is to examine the impact of a range of opioid-related policies on hospital admissions and emergency department (ED) presentations in Victoria, Australia. In particular, the study aims to understand the effect of various opioid policies and opioid-prescribing changes on (1) the number and rates of ED presentations and hospital admissions attributed to substance use (ie, opioid and nonopioid related) or mental ill-health (eg, suicide, self-harm, anxiety, and depression), (2) the association between differing opioid dose trajectories and the likelihood of ED presentations and hospital admissions related to substance use and mental ill-health, and (3) whether changes in an individual's opioid prescribing change the risk related to ED presentations and hospital admissions related to substance use and mental ill-health. Methods: We will conduct a population-level linked data study. General practice health records obtained from the Population Level Analysis and Reporting platform are linked with person-level data from 3 large hospital networks in Victoria, Australia. Interrupted time series analysis will be used to examine the impact of opioid policies on a range of harms, including the rates of presentations related to substance use (opioid and nonopioid) and mental ill-health among the primary care cohort. Group-based trajectory modeling and a case-crossover design will be used to further explore the impact of changes in opioid dosage and other covariates on opioid and nonopioid poisonings and mental ill-health--related presentations at the patient level. Results: Given that this paper serves as a protocol, there are currently no results available. The deidentified primary health data were sourced from electronic medical records of approximately 4,717,000 patients from 542 consenting general practices over a 6-year period (2017-2022). The submission of results for publication is planned for early 2024. Conclusions: This study will add to the limited evidence base to help understand the impact of opioid policies in Australia, including whether intended or unintended outcomes are occurring as a result. Trial Registration: EU PAS Register EUPAS104005; https://www.encepp.eu/encepp/viewResource.htm?id=104006 International Registered Report Identifier (IRRID): DERR1-10.2196/51825 ", doi="10.2196/51825", url="https://www.researchprotocols.org/2023/1/e51825", url="http://www.ncbi.nlm.nih.gov/pubmed/37847553" } @Article{info:doi/10.2196/48189, author="Lustig, Andrew and Brookes, Gavin", title="Corpus-Based Discourse Analysis of a Reddit Community of Users of Crystal Methamphetamine: Mixed Methods Study", journal="JMIR Infodemiology", year="2023", month="Sep", day="29", volume="3", pages="e48189", keywords="methamphetamine", keywords="social media", keywords="substance-related disorders", keywords="discourse analysis", keywords="mental health", keywords="mixed methods", keywords="corpus analysis", keywords="web-based health", abstract="Background: Methamphetamine is a highly addictive stimulant that affects the central nervous system. Crystal methamphetamine is a form of the drug resembling glass fragments or shiny bluish-white rocks that can be taken through smoking, swallowing, snorting, or injecting the powder once it has been dissolved in water or alcohol. Objective: The objective of this study is to examine how identities are socially (discursively) constructed by people who use methamphetamine within a subreddit for people who regularly use crystal meth. Methods: Using a mixed methods approach, we analyzed 1000 threads (318,422 words) from a subreddit for regular crystal meth users. The qualitative component of the analysis used concordancing and corpus-based discourse analysis to identify discursive themes informed by assemblage theory. The quantitative portion of the analysis used corpus linguistic techniques including keyword analysis to identify words occurring with statistically marked frequency in the corpus and collocation analysis to analyze their discursive context. Results: Our findings reveal that the subreddit contributors use a rich and varied lexicon to describe crystal meth and other substances, ranging from a neuroscientific register (eg, methamphetamine and dopamine) to informal vernacular (eg, meth, dope, and fent) and commercial appellations (eg, Adderall and Seroquel). They also use linguistic resources to construct symbolic boundaries between different types of methamphetamine users, differentiating between the esteemed category of ``functional addicts'' and relegating others to the stigmatized category of ``tweakers.'' In addition, contributors contest the dominant view that methamphetamine use inevitably leads to psychosis, arguing instead for a more nuanced understanding that considers the interplay of factors such as sleep deprivation, poor nutrition, and neglected hygiene. Conclusions: The subreddit contributors' discourse offers a ``set and setting'' perspective, which provides a fresh viewpoint on drug-induced psychosis and can guide future harm reduction strategies and research. In contrast to this view, many previous studies overlook the real-world complexities of methamphetamine use, perhaps due to the use of controlled experimental settings. Actual drug use, intoxication, and addiction are complex, multifaceted, and elusive phenomena that defy straightforward characterization. ", doi="10.2196/48189", url="https://infodemiology.jmir.org/2023/1/e48189", url="http://www.ncbi.nlm.nih.gov/pubmed/37773617" } @Article{info:doi/10.2196/48976, author="Fossouo Tagne, Joel and Yakob, Amin Reginald and Mcdonald, Rachael and Wickramasinghe, Nilmini", title="A Web-Based Tool to Report Adverse Drug Reactions by Community Pharmacists in Australia: Usability Testing Study", journal="JMIR Form Res", year="2023", month="Sep", day="29", volume="7", pages="e48976", keywords="ADR", keywords="adverse drug reaction", keywords="pharmacovigilance", keywords="community pharmacy", keywords="digital health evaluation", keywords="usability testing", abstract="Background: Adverse drug reactions (ADRs) are unintended and harmful events associated with medication use. Despite their significance in postmarketing surveillance, quality improvement, and drug safety research, ADRs are vastly underreported. Enhanced digital-based communication of ADR information to regulators and among care providers could significantly improve patient safety. Objective: This paper presents a usability evaluation of the commercially available GuildCare Adverse Event Recording system, a web-based ADR reporting system widely used by community pharmacists (CPs) in Australia. Methods: We developed a structured interview protocol encompassing remote observation, think-aloud moderating techniques, and retrospective questioning to gauge the overall user experience, complemented by the System Usability Scale (SUS) assessment. Thematic analysis was used to analyze field notes from the interviews. Results: A total of 7 CPs participated in the study, who perceived the system to have above-average usability (SUS score of 68.57). Nonetheless, the structured approach to usability testing unveiled specific functional and user interpretation issues, such as unnecessary information, lack of system clarity, and redundant data fields---critical insights not captured by the SUS results. Design elements like drop-down menus, free-text entry, checkboxes, and prefilled or auto-populated data fields were perceived as useful for enhancing system navigation and facilitating ADR reporting. Conclusions: The user-centric design of technology solutions, like the one discussed herein, is crucial to meeting CPs' information needs and ensuring effective ADR reporting. Developers should adopt a structured approach to usability testing during the developmental phase to address identified issues comprehensively. Such a methodological approach may promote the adoption of ADR reporting systems by CPs and ultimately enhance patient safety. ", doi="10.2196/48976", url="https://formative.jmir.org/2023/1/e48976", url="http://www.ncbi.nlm.nih.gov/pubmed/37773620" } @Article{info:doi/10.2196/50085, author="Belhassen, Manon and Nolin, Maeva and Jacoud, Flore and Marant Micallef, Claire and Van Ganse, Eric", title="Trajectories of Controller Therapy Use Before and After Asthma-Related Hospitalization in Children and Adults: Population-Based Retrospective Cohort Study", journal="JMIR Public Health Surveill", year="2023", month="Sep", day="26", volume="9", pages="e50085", keywords="asthma", keywords="hospitalization", keywords="inhaled corticosteroids", keywords="trajectories", keywords="quality of care", keywords="clustering", abstract="Background: Inappropriate use of inhaled corticosteroids (ICSs) for asthma impairs control and may cause exacerbation, including asthma-related hospitalization (ARH). In prospective studies, ICS use peaked around ARH, but information on routine care use is limited. Since ARH is a major outcome, controller therapy use in routine care before and after ARH should be documented. Objective: This study aimed to distinguish ICS use typologies (trajectories) before and after ARH, and assess their relationships with sociodemographic, disease, and health care characteristics. Methods: A retrospective cohort study was performed using a 1\% random sample of the French claims database. All patients hospitalized for asthma between January 01, 2013, and December 31, 2015, were classified as either children (aged 1-10 years) or teens/adults (aged ?11 years). Health care resource use was assessed between 24 and 12 months before ARH. ICS use was computed with the Continuous Measures of Medication Acquisition-7 (CMA7) for the 4 quarters before and after ARH. Initially, the overall impact of hospitalization on the CMA7 value was studied using a segmented regression analysis in both children and teens/adults. Then, group-based trajectory modeling differentiated the groups with similar ICS use. We tested different models having 2 to 5 distinct trajectory groups before selecting the most appropriate trajectory form. We finally selected the model with the lowest Bayesian Information Criterion, the highest proportion of patients in each group, and the maximum estimated probability of assignment to a specific group. Results: Overall, 863 patients were included in the final study cohort, of which 447 (51.8\%) were children and 416 (48.2\%) were teens/adults. In children, the average CMA7 value was 12.6\% at the start of the observation period, and there was no significant quarter-to-quarter change in the value (P=.14) before hospitalization. Immediately after hospitalization, the average CMA7 value rose by 34.9\% (P=.001), before a significant decrease (P=.01) of 7.0\% per quarter. In teens/adults, the average CMA7 value was 31.0\% at the start, and there was no significant quarter-to-quarter change in the value (P=.08) before hospitalization. Immediately after hospitalization, the average CMA7 value rose by 26.9\% (P=.002), before a significant decrease (P=.01) of 7.0\% per quarter. We identified 3 and 5 trajectories before ARH in children and adults, respectively, and 5 after ARH for both groups. Trajectories were related to sociodemographic characteristics (particularly, markers of social deprivation) and to potentially inappropriate health care, such as medical management and choice of therapy. Conclusions: Although ARH had an overall positive impact on ICS use trajectories, the effect was often transient, and patient behaviors were heterogeneous. Along with overall trends, distinct trajectories were identified, which were related to specific patients and health care characteristics. Our data reinforce the evidence that inappropriate use of ICS paves the way for ARH. ", doi="10.2196/50085", url="https://publichealth.jmir.org/2023/1/e50085", url="http://www.ncbi.nlm.nih.gov/pubmed/37751244" } @Article{info:doi/10.2196/43630, author="Fisher, Andrew and Young, Maclaren Matthew and Payer, Doris and Pacheco, Karen and Dubeau, Chad and Mago, Vijay", title="Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework", journal="J Med Internet Res", year="2023", month="Sep", day="19", volume="25", pages="e43630", keywords="early warning system", keywords="social media", keywords="law enforcement", keywords="public health", keywords="new psychoactive substances", keywords="development", keywords="drug", keywords="dosage", keywords="Canada", keywords="Twitter", keywords="poisoning", keywords="monitoring", keywords="community", keywords="public safety", keywords="machine learning", keywords="Fleiss", keywords="tweet", keywords="tweet annotations", keywords="pharmacology", keywords="addiction", abstract="Background: A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada. Objective: The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities. Methods: To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2. Results: When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of {\textasciitilde}84.5\%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of {\textasciitilde}94.1\%) with the subject matter experts. Conclusions: This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain. ", doi="10.2196/43630", url="https://www.jmir.org/2023/1/e43630", url="http://www.ncbi.nlm.nih.gov/pubmed/37725410" } @Article{info:doi/10.2196/47068, author="Golder, Su and O'Connor, Karen and Wang, Yunwen and Gonzalez Hernandez, Graciela", title="The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review", journal="JMIR Res Protoc", year="2023", month="Aug", day="2", volume="12", pages="e47068", keywords="adverse event", keywords="pharmacovigilance", keywords="social media", keywords="real-world data", keywords="scoping review", keywords="protocol", keywords="review method", keywords="pharmacology", keywords="pharmaceutics", keywords="pharmacy", keywords="adverse drug event", keywords="adverse drug reaction", abstract="Background: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient's quality of life and adherence to intervention. Monitoring medication safety, however, is challenging. Social media may be a useful adjunct for obtaining real-world data on ADEs. While many studies have been undertaken to detect adverse events on social media, a consensus has not yet been reached as to the value of social media in pharmacovigilance or its role in pharmacovigilance in relation to more traditional data sources. Objective: The aim of the study is to evaluate and characterize the use of social media in ADE detection and pharmacovigilance as compared to other data sources. Methods: A scoping review will be undertaken. We will search 11 bibliographical databases as well as Google Scholar, hand-searching, and forward and backward citation searching. Records will be screened in Covidence by 2 independent reviewers at both title and abstract stage as well as full text. Studies will be included if they used any type of social media (such as Twitter or patient forums) to detect any type of adverse event associated with any type of medication and then compared the results from social media to any other data source (such as spontaneous reporting systems or clinical literature). Data will be extracted using a data extraction sheet piloted by the authors. Important data on the types of methods used (such as machine learning), any limitations of the methods used, types of adverse events and drugs searched for and included, availability of data and code, details of the comparison data source, and the results and conclusions will be extracted. Results: We will present descriptive summary statistics as well as identify any patterns in the types and timing of ADEs detected, including but not limited to the similarities and differences in what is reported, gaps in the evidence, and the methods used to extract ADEs from social media data. We will also summarize how the data from social media compares to conventional data sources. The literature will be organized by the data source for comparison. Where possible, we will analyze the impact of the types of adverse events, the social media platform used, and the methods used. Conclusions: This scoping review will provide a valuable summary of a large body of research and important information for pharmacovigilance as well as suggest future directions of further research in this area. Through the comparisons with other data sources, we will be able to conclude the added value of social media in monitoring adverse events of medications, in terms of type of adverse events and timing. International Registered Report Identifier (IRRID): PRR1-10.2196/47068 ", doi="10.2196/47068", url="https://www.researchprotocols.org/2023/1/e47068", url="http://www.ncbi.nlm.nih.gov/pubmed/37531158" } @Article{info:doi/10.2196/48405, author="Parker, A. Maria and Valdez, Danny and Rao, K. Varun and Eddens, S. Katherine and Agley, Jon", title="Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses", journal="J Med Internet Res", year="2023", month="Jul", day="28", volume="25", pages="e48405", keywords="Twitter", keywords="LDA", keywords="drug use", keywords="digital epidemiology", keywords="unsupervised analysis", keywords="tweet", keywords="tweets", keywords="social media", keywords="epidemiology", keywords="epidemiological", keywords="machine learning", keywords="text mining", keywords="data mining", keywords="pharmacy", keywords="pharmaceutic", keywords="pharmaceutical", keywords="pharmaceuticals", keywords="drug", keywords="prescription", keywords="NLP", keywords="natural language processing", abstract="Background: Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. Objective: This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. Methods: This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug--related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. Results: We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. Conclusions: Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter. ", doi="10.2196/48405", url="https://www.jmir.org/2023/1/e48405", url="http://www.ncbi.nlm.nih.gov/pubmed/37505795" } @Article{info:doi/10.2196/46767, author="Wang, Wenjing and Zhao, Shengnan and Wu, Yaxin and Duan, Wenshan and Li, Sibo and Li, Zhen and Guo, Caiping and Wang, Wen and Zhang, Tong and Wu, Hao and Huang, Xiaojie", title="Safety and Efficacy of Long-Acting Injectable Agents for HIV-1: Systematic Review and Meta-Analysis", journal="JMIR Public Health Surveill", year="2023", month="Jul", day="27", volume="9", pages="e46767", keywords="long-acting cabotegravir", keywords="CAB-LA", keywords="long-acting rilpivirine", keywords="RPV-LA", keywords="pre-exposure prophylaxis", keywords="PrEP", keywords="treatment", keywords="long-term suppression", abstract="Background: HIV-1 infection continues to affect global health. Although antiretrovirals can reduce the viral load or prevent HIV-1 infection, current drugs require daily oral use with a high adherence level. Long-acting antiretrovirals (LA-ARVs) significantly improve medication adherence and are essential for HIV-1 prophylaxis and therapy. Objective: This study aimed to investigate the safety and efficacy of long-acting cabotegravir (CAB-LA) and long-acting rilpivirine (RPV-LA) in the prevention and treatment of HIV-1 infection. Methods: PubMed, Embase, and the Cochrane Library were searched for studies from database inception to November 12, 2022. We included studies that reported efficacy and safety data on LA-ARV intervention in people living with HIV and excluded reviews, animal studies, and articles with missing or duplicate data. Virological suppression was defined as plasma viral load <50 copies/mL 6 months after antiviral therapy initiation. We extracted outcomes for analysis and expressed dichotomous data as risk ratios (RRs) and continuous data as mean differences. Depending on the heterogeneity assessment, a fixed- or random-effects model was used for data synthesis. We performed subgroup analyses of the partial safety and efficacy outcomes of CAB-LA+RPV-LA. The protocol was registered with the Open Science Framework. Results: We included 12 trials comprising 10,957 individuals, of which 7 were prevention trials and 5 were treatment trials. CAB-LA and RPV-LA demonstrated safety profiles comparable with those of the placebo in terms of adverse event--related withdrawal. Moreover, the efficacy data showed that CAB-LA had a better effect on HIV-1 prevention than tenofovir disoproxil fumarate--emtricitabine (17/5161, 0.33\% vs 75/5129, 1.46\%; RR 0.21, 95\% CI 0.07-0.61; I2=70\%). Although CAB-LA+RPV-LA had more drug-related adverse events (556/681, 81.6\% vs 37/598, 6.2\%; RR 12.50, 95\% CI 3.98-39.23; I2=85\%), a mild or moderate injection site reaction was the most common reaction, and its frequency decreased over time. The efficacy of CAB-LA+RPV-LA was comparable with that of daily oral drugs at 48 and 96 weeks (1302/1424, 91.43\% vs 915/993, 92.2\%; RR 0.99, 95\% CI 0.97-1.02; I2=0\%), and a high level of virological suppression of 80.9\% (186/230) was maintained even after 5 years of LA-ARV use. Similar efficacy outcomes were observed in both treatment-naive and treatment-experienced patients (849/911, 93.2\% vs 615/654, 94\%; RR 0.99, 95\% CI 0.96-1.02; I2=0\%). According to the questionnaires, more than 85\% of people living with HIV favored LA-ARVs. Conclusions: LA-ARVs showed favorable safety profiles for both the prevention and treatment of HIV-1 infection and were well tolerated. CAB-LA has more satisfactory efficacy than tenofovir disoproxil fumarate--emtricitabine, significantly reducing the rate of HIV-1 infection. CAB-LA+RPV-LA maintains virological suppression for a long time and may be a viable switching strategy with enhanced public health benefits by reducing transmission. However, further trials are required to confirm the efficacy of these drugs. ", doi="10.2196/46767", url="https://publichealth.jmir.org/2023/1/e46767", url="http://www.ncbi.nlm.nih.gov/pubmed/37498645" } @Article{info:doi/10.2196/40616, author="Schulz, Johannes Peter and Crosignani, Francesca and Petrocchi, Serena", title="Critical Test of the Beneficial Consequences of Lifting the Ban on Direct-to-Consumer Advertising for Prescription Drugs in Italy: Experimental Exposure and Questionnaire Study", journal="J Med Internet Res", year="2023", month="Jul", day="17", volume="25", pages="e40616", keywords="eDTCA", keywords="health literacy", keywords="knowledge", keywords="empowerment", keywords="health information", keywords="antidepressant", keywords="depression", keywords="depressive disorder", keywords="pharmaceutical", keywords="advertise", keywords="advertising", keywords="drug", keywords="marketing", keywords="patient education", keywords="consumer", keywords="health education", abstract="Background: There are only two countries in the world (the United States and New Zealand) that allow the pharmaceutical branch to advertise prescription medication directly to consumers. There is pressure on governments to allow direct-to-consumer advertising (DTCA) for prescription drugs elsewhere too. One argument the industry uses frequently is the claim that exposure to DCTA, through various methods and occasions, is supposed to improve customers' knowledge of a disease and treatment. This argument has been part of the health care community's wider discussion of whether DTCA of prescription drugs benefits the population's general interest or is only an attempt to increase the sales of the pharmaceutical branch. Belief in true learning by DTCA is rooted in concepts of empowered consumers and their autonomous and empowered decision-making. Objective: In this study, we tested the hypotheses that contact with DTCA increases recipients' literacy/knowledge, especially regarding the side effects of treatment (hypothesis 1), and empowerment (hypothesis 2). We further hypothesized that DTCA exposure would not increase depression knowledge (ie, about treatments, symptoms, and prevalence) (hypothesis 3). Methods: A snowball sample of 180 participants was randomly split into three experimental groups receiving (1) a traditional information sheet, (2) a DTCA video clip for an antidepressant prescription drug, or (3) both. The video was original material from the United States translated into Italian for the experiment. Dependent variables were measures of depression knowledge (regarding treatments, symptoms and prevalence, and antidepressant side effects), depression literacy, and empowerment. Results: None of the experimental groups differed significantly from the others in the empowerment measure (hypothesis 2 not confirmed). Partial confirmation of hypothesis 1 was obtained. Lower values on the depression literacy scale were obtained when participants had been given the video compared to the sheet condition. However, the general depression knowledge and its subscale on side effects reached higher scores when participants were exposed to the DTCA, alone or in combination with the information sheet. Finally, participants showed lower scores on knowledge about treatment and symptoms or prevalence after watching the video compared to the sheet condition (hypothesis 3 confirmed). Symptoms and prevalence knowledge increased only when the video was presented in combination with the sheet. Conclusions: There is no evidence for an increase in empowerment following DTCA exposure. An increase in knowledge of the side effects of the medication was observed in the group exposed to the DTCA video. This was the only result that confirmed the hypothesis of the beneficial effect of DTCA videos on knowledge. Written information proved to be the most suitable way to convey knowledge on treatments and symptoms prevalence. Our findings support the necessity of studying health literacy and patient empowerment together and the consequences of such an increase in knowledge in terms of help-seeking behavior. ", doi="10.2196/40616", url="https://www.jmir.org/2023/1/e40616", url="http://www.ncbi.nlm.nih.gov/pubmed/37459159" } @Article{info:doi/10.2196/45263, author="Thai-Van, Hung and Valnet-Rabier, Marie-Blanche and Anciaux, Ma{\"e}va and Lambert, Aude and Maurier, Ana{\"i}s and Cottin, Judith and Pietri, Tessa and Dest{\`e}re, Alexandre and Damin-Pernik, Marl{\`e}ne and Perrouin, Fanny and Bagheri, Haleh", title="Safety Signal Generation for Sudden Sensorineural Hearing Loss Following Messenger RNA COVID-19 Vaccination: Postmarketing Surveillance Using the French Pharmacovigilance Spontaneous Reporting Database", journal="JMIR Public Health Surveill", year="2023", month="Jul", day="14", volume="9", pages="e45263", keywords="mRNA COVID-19 vaccine", keywords="COVID-19", keywords="messenger RNA", keywords="tozinameran", keywords="elasomeran", keywords="sudden sensorineural hearing loss", keywords="audiogram", keywords="positive rechallenge", keywords="spontaneous reporting", keywords="postmarketing", keywords="surveillance", keywords="pharmacovigilance", abstract="Background: The World Health Organization recently described sudden sensorineural hearing loss (SSNHL) as a possible adverse effect of COVID-19 vaccines. Recent discordant pharmacoepidemiologic studies invite robust clinical investigations of SSNHL after COVID-19 messenger RNA (mRNA) vaccines. This postmarketing surveillance study, overseen by French public health authorities, is the first to clinically document postvaccination SSNHL and examine the role of potential risk factors. Objective: This nationwide study aimed to assess the relationship between SSNHL and exposure to mRNA COVID-19 vaccines and estimate the reporting rate (Rr) of SSNHL after mRNA vaccination per 1 million doses (primary outcome). Methods: We performed a retrospective review of all suspected cases of SSNHL after mRNA COVID-19 vaccination spontaneously reported in France between January 2021 and February 2022 based on a comprehensive medical evaluation, including the evaluation of patient medical history, side and range of hearing loss, and hearing recovery outcomes after a minimum period of 3 months. The quantification of hearing loss and assessment of hearing recovery outcomes were performed according to a grading system modified from the Siegel criteria. A cutoff of 21 days was used for the delay onset of SSNHL. The primary outcome was estimated using the total number of doses of each vaccine administered during the study period in France as the denominator. Results: From 400 extracted cases for tozinameran and elasomeran, 345 (86.3\%) spontaneous reports were selected. After reviewing complementary data, 49.6\% (171/345) of documented cases of SSNHL were identified. Of these, 83\% (142/171) of SSNHL cases occurred after tozinameran vaccination: Rr=1.45/1,000,000 injections; no difference for the rank of injections; complete recovery in 22.5\% (32/142) of cases; median delay onset before day 21=4 days (median age 51, IQR 13-83 years); and no effects of sex. A total of 16.9\% (29/171) of SSNHL cases occurred after elasomeran vaccination: Rr=1.67/1,000,000 injections; rank effect in favor of the first injection (P=.03); complete recovery in 24\% (7/29) of cases; median delay onset before day 21=8 days (median age 47, IQR 33-81 years); and no effects of sex. Autoimmune, cardiovascular, or audiovestibular risk factors were present in approximately 29.8\% (51/171) of the cases. SSNHL was more often unilateral than bilateral for both mRNA vaccines (P<.001 for tozinameran; P<.003 for elasomeran). There were 13.5\% (23/142) of cases of profound hearing loss, among which 74\% (17/23) did not recover a serviceable ear. A positive rechallenge was documented for 8 cases. Conclusions: SSNHL after COVID-19 mRNA vaccines are very rare adverse events that do not call into question the benefits of mRNA vaccines but deserve to be known given the potentially disabling impact of sudden deafness. Therefore, it is essential to properly characterize postinjection SSNHL, especially in the case of a positive rechallenge, to provide appropriate individualized recommendations. ", doi="10.2196/45263", url="https://publichealth.jmir.org/2023/1/e45263", url="http://www.ncbi.nlm.nih.gov/pubmed/37071555" } @Article{info:doi/10.2196/43776, author="Salazar, Alejandro and Moreno-Pulido, Soledad and Prego-Meleiro, Pablo and Henares-Montiel, Jes{\'u}s and Pulido, Jos{\'e} and Donat, Marta and Sotres-Fernandez, Gabriel and Sordo, Luis", title="Correlation Between Opioid Drug Prescription and Opioid-Related Mortality in Spain as a Surveillance Tool: Ecological Study", journal="JMIR Public Health Surveill", year="2023", month="Jun", day="28", volume="9", pages="e43776", keywords="opioid", keywords="overdose", keywords="drug overdose", keywords="opioid-related deaths", keywords="mortality", keywords="tramadol", keywords="fentanyl", keywords="substance use", keywords="substance misuse", keywords="substance abuse", keywords="ecological study", keywords="death", abstract="Background: Opioid drug prescription (ODP) and opioid-related mortality (ORM) have increased in Spain. However, their relationship is complex, as ORM is registered without considering the type of opioid (legal or illegal). Objective: This ecological study aimed to examine the correlation between ODP and ORM in Spain and discuss their usefulness as a surveillance tool. Methods: This was an ecological descriptive study using retrospective annual data (2000-2019) from the Spanish general population. Data were collected from people of all ages. Information on ODP was obtained from the Spanish Medicines Agency in daily doses per 1000 inhabitants per day (DHD) for total ODP, total ODP excluding those with better safety protocols (codeine and tramadol), and each opioid drug separately. Rates of ORM (per 1,000,000 inhabitants) were calculated based on deaths registered (International Classification of Diseases, 10th Revision codes) as opioid poisoning by the National Statistics Institute, derived from the drug data recorded by medical examiners in death certificates. Opioid-related deaths were considered to be those that indicated opioid consumption (accidental, infringed, or self-inflicted) as the main cause of death: death due to accidental poisoning (X40-X44), intentional self-inflicted poisoning (X60-X64), drug-induced aggression (X85), and poisoning of undetermined intention (Y10-Y14). A descriptive analysis was carried out, and correlations between the annual rates of ORM and DHD of the prescribed opioid drugs globally, excluding medications of the least potential risk of overdose and lowest treatment tier, were analyzed using Pearson linear correlation coefficient. Their temporal evolution was analyzed using cross-correlations with 24 lags and the cross-correlation function. The analyses were carried out using Stata and StatGraphics Centurion 19. Results: The rate of ORM (2000-2019) ranged between 14 and 23 deaths per 1,000,000 inhabitants, with a minimum in 2006 and an increasing trend starting in 2010. The ODP ranged between 1.51 to 19.94 DHD. The rates of ORM were directly correlated with the DHD of total ODP (r=0.597; P=.006), total ODP without codeine and tramadol (r=0.934; P<.001), and every prescribed opioid except buprenorphine (P=.47). In the time analysis, correlations between DHD and ORM were observed in the same year, although not statistically significant (all P?.05). Conclusions: There is a correlation between greater availability of prescribed opioid drugs and an increase in opioid-related deaths. The correlation between ODP and ORM may be a useful tool in monitoring legal opiates and possible disturbances in the illegal market. The role of tramadol (an easily prescribed opioid) is important in this correlation, as is that of fentanyl (the strongest opioid). Measures stronger than recommendations need to be taken to reduce off-label prescribing. This study shows that not only is opioid use directly related to the prescribing of opioid drugs above what is desirable but also an increase in deaths. ", doi="10.2196/43776", url="https://publichealth.jmir.org/2023/1/e43776", url="http://www.ncbi.nlm.nih.gov/pubmed/37379061" } @Article{info:doi/10.2196/45582, author="Alexa, Maria Jennifer and Richter, Matthias and Bertsche, Thilo", title="Enhancing Evidence-Based Pharmacy by Comparing the Quality of Web-Based Information Sources to the EVInews Database: Randomized Controlled Trial With German Community Pharmacists", journal="J Med Internet Res", year="2023", month="Jun", day="21", volume="25", pages="e45582", keywords="databases", keywords="electronic information", keywords="evidence-based pharmacy practice", keywords="evidence-based pharmacy", keywords="evidence-based practice", keywords="external evidence", keywords="health information quality", keywords="information tools", keywords="newsletter", keywords="online survey", keywords="pharmacist", keywords="self-medication counseling", keywords="self-medication", keywords="utilization", abstract="Background: Self-medication counseling in community pharmacies plays a crucial role in health care. Counseling advice should therefore be evidence-based. Web-based information and databases are commonly used as electronic information sources. EVInews is a self-medication--related information tool consisting of a database and monthly published newsletters for pharmacists. Little is known about the quality of pharmacists' electronic information sources for evidence-based self-medication counseling. Objective: Our aim was to investigate the quality of community pharmacists' web-based search results for self-medication--related content in comparison with the EVInews database, based on an adjusted quality score for pharmacists. Methods: After receiving ethics approval, we performed a quantitative web-based survey with a search task as a prospective randomized, controlled, and unblinded trial. For the search task, participants were instructed to search for evidence-based information to verify 6 health-related statements from 2 typical self-medication indications. Pharmacists across Germany were invited via email to participate. After providing written informed consent, they were automatically, randomly assigned to use either web-based information sources of their choice without the EVInews database (web group) or exclusively the EVInews database (EVInews group). The quality of the information sources that were used for the search task was then assessed by 2 evaluators using a quality score ranging from 100\% (180 points, all predefined criteria fulfilled) to 0\% (0 points, none of the predefined criteria fulfilled). In case of assessment discrepancies, an expert panel consisting of 4 pharmacists was consulted. Results: In total, 141 pharmacists were enrolled. In the Web group (n=71 pharmacists), the median quality score was 32.8\% (59.0 out of 180.0 points; IQR 23.0-80.5). In the EVInews group (n=70 pharmacists), the median quality score was significantly higher (85.3\%; 153.5 out of 180.0 points; P<.001) and the IQR was smaller (IQR 125.1-157.0). Fewer pharmacists completed the entire search task in the Web group (n=22) than in the EVInews group (n=46). The median time to complete the search task was not significantly different between the Web group (25.4 minutes) and the EVInews group (19.7 minutes; P=.12). The most frequently used web-based sources (74/254, 29.1\%) comprised tertiary literature. Conclusions: The median quality score of the web group was poor, and there was a significant difference in quality scores in favor of the EVInews group. Pharmacists' web-based and self-medication--related information sources often did not meet standard quality requirements and showed considerable variation in quality. Trial Registration: German Clinical Trials Register DRKS00026104; https://drks.de/search/en/trial/DRKS00026104 ", doi="10.2196/45582", url="https://www.jmir.org/2023/1/e45582", url="http://www.ncbi.nlm.nih.gov/pubmed/37342085" } @Article{info:doi/10.2196/45246, author="Tang, Anne Leigh and Korona-Bailey, Jessica and Zaras, Dimitrios and Roberts, Allison and Mukhopadhyay, Sutapa and Espy, Stephen and Walsh, G. Colin", title="Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study", journal="JMIR Public Health Surveill", year="2023", month="May", day="19", volume="9", pages="e45246", keywords="fatal drug overdose", keywords="natural language processing", keywords="surveillance", keywords="Tennessee", keywords="State Unintentional Drug Overdose Reporting System", keywords="SUDORS", abstract="Background: Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to preliminary death scene investigation reports) and may serve as early data sources for identifying fatal drug overdoses. To facilitate timely fatal overdose reporting, natural language processing was applied to narrative texts from autopsies. Objective: This study aimed to develop a natural language processing--based model that predicts the likelihood that an autopsy report narrative describes an accidental or undetermined fatal drug overdose. Methods: Autopsy reports of all manners of death (2019-2021) were obtained from the Tennessee Office of the State Chief Medical Examiner. The text was extracted from autopsy reports (PDFs) using optical character recognition. Three common narrative text sections were identified, concatenated, and preprocessed (bag-of-words) using term frequency--inverse document frequency scoring. Logistic regression, support vector machine (SVM), random forest, and gradient boosted tree classifiers were developed and validated. Models were trained and calibrated using autopsies from 2019 to 2020 and tested using those from 2021. Model discrimination was evaluated using the area under the receiver operating characteristic, precision, recall, F1-score, and F2-score (prioritizes recall over precision). Calibration was performed using logistic regression (Platt scaling) and evaluated using the Spiegelhalter z test. Shapley additive explanations values were generated for models compatible with this method. In a post hoc subgroup analysis of the random forest classifier, model discrimination was evaluated by forensic center, race, age, sex, and education level. Results: A total of 17,342 autopsies (n=5934, 34.22\% cases) were used for model development and validation. The training set included 10,215 autopsies (n=3342, 32.72\% cases), the calibration set included 538 autopsies (n=183, 34.01\% cases), and the test set included 6589 autopsies (n=2409, 36.56\% cases). The vocabulary set contained 4002 terms. All models showed excellent performance (area under the receiver operating characteristic ?0.95, precision ?0.94, recall ?0.92, F1-score ?0.94, and F2-score ?0.92). The SVM and random forest classifiers achieved the highest F2-scores (0.948 and 0.947, respectively). The logistic regression and random forest were calibrated (P=.95 and P=.85, respectively), whereas the SVM and gradient boosted tree classifiers were miscalibrated (P=.03 and P<.001, respectively). ``Fentanyl'' and ``accident'' had the highest Shapley additive explanations values. Post hoc subgroup analyses revealed lower F2-scores for autopsies from forensic centers D and E. Lower F2-score were observed for the American Indian, Asian, ?14 years, and ?65 years subgroups, but larger sample sizes are needed to validate these findings. Conclusions: The random forest classifier may be suitable for identifying potential accidental and undetermined fatal overdose autopsies. Further validation studies should be conducted to ensure early detection of accidental and undetermined fatal drug overdoses across all subgroups. ", doi="10.2196/45246", url="https://publichealth.jmir.org/2023/1/e45246", url="http://www.ncbi.nlm.nih.gov/pubmed/37204824" } @Article{info:doi/10.2196/39700, author="Bota, Brianne A. and Bettinger, A. Julie and Sarfo-Mensah, Shirley and Lopez, Jimmy and Smith, P. David and Atkinson, M. Katherine and Bell, Cameron and Marty, Kim and Serhan, Mohamed and Zhu, T. David and McCarthy, E. Anne and Wilson, Kumanan", title="Comparing the Use of a Mobile App and a Web-Based Notification Platform for Surveillance of Adverse Events Following Influenza Immunization: Randomized Controlled Trial", journal="JMIR Public Health Surveill", year="2023", month="May", day="8", volume="9", pages="e39700", keywords="active participant--centered reporting", keywords="health technology", keywords="adverse event reporting", keywords="mobile apps", keywords="immunization", keywords="vaccine", keywords="safety", keywords="influenza", keywords="campaign", keywords="apps", keywords="mobile", keywords="surveillance", keywords="pharmacovigilance", abstract="Background: Vaccine safety surveillance is a core component of?vaccine pharmacovigilance. In Canada, active, participant-centered vaccine surveillance is available for influenza vaccines and has been used for COVID-19 vaccines. Objective: The objective of this study is to evaluate the effectiveness and feasibility of using a mobile app for reporting participant-centered seasonal influenza adverse events following immunization (AEFIs) compared to a web-based notification system. Methods: Participants were randomized to influenza vaccine safety reporting via a mobile app or a web-based notification platform. All participants were invited to complete a user experience survey. Results: Among the 2408 randomized participants, 1319 (54\%) completed their safety survey 1 week after vaccination, with a higher completion rate among the web-based notification platform users (767/1196, 64\%) than among mobile app users (552/1212, 45\%; P<.001). Ease-of-use ratings were high for the web-based notification platform users (99\% strongly agree or agree) and 88.8\% of them strongly agreed or agreed that the system made reporting AEFIs easier. Web-based notification platform users supported the statement that a web-based notification-only approach would make it easier for public health professionals to detect vaccine safety signals (91.4\%, agreed or strongly agreed). Conclusions: Participants in this study were significantly more likely to respond to a web-based safety survey rather than within a mobile app. These results suggest that mobile apps present an additional barrier for use compared to the web-based notification--only approach. Trial Registration: ClinicalTrials.gov NCT05794113; https://clinicaltrials.gov/show/NCT05794113 ", doi="10.2196/39700", url="https://publichealth.jmir.org/2023/1/e39700", url="http://www.ncbi.nlm.nih.gov/pubmed/37155240" } @Article{info:doi/10.2196/44870, author="Nishiyama, Tomohiro and Yada, Shuntaro and Wakamiya, Shoko and Hori, Satoko and Aramaki, Eiji", title="Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach", journal="J Med Internet Res", year="2023", month="May", day="3", volume="25", pages="e44870", keywords="data mining", keywords="machine learning", keywords="medication noncompliance", keywords="natural language processing", keywords="pharmacovigilance", keywords="transfer learning", keywords="text classification", abstract="Background: Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media--based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients. Objective: This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance. Methods: This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs). Results: The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small. Conclusions: The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured. ", doi="10.2196/44870", url="https://www.jmir.org/2023/1/e44870", url="http://www.ncbi.nlm.nih.gov/pubmed/37133915" } @Article{info:doi/10.2196/35865, author="Amorim, de F{\'a}bio Jorge Ramalho and Valen{\c{c}}a-Feitosa, Fernanda and Rios, Cardoso Marcos and Santos Souza, Adriano Carlos and Barros, Cunha Izadora Menezes da and Oliveira-Filho, de Alfredo Dias and Lyra-J{\'u}nior, de Divaldo Pereira", title="The Pharmacoeconomic Impact of Pharmaceutical Care in the Hospital: Protocol for an Overview of Systematic Reviews", journal="JMIR Res Protoc", year="2023", month="Apr", day="21", volume="12", pages="e35865", keywords="pharmacoeconomics", keywords="pharmaceutical care", keywords="hospital", keywords="overview", keywords="cost-effectiveness", keywords="cost benefit", keywords="cost-utility", abstract="Background: The clinical activities developed by pharmacists in a hospital environment can improve health outcomes and generate savings for hospitals. However, to determine whether pharmaceutical interventions are cost effective, it is essential to define a method according to which cost-effectiveness is intended to be measured. In addition, the quality of economic assessments and the amount of information present in systematic reviews in the literature make it difficult to analyze the effects of this intervention. Objective: This paper aims to provide an overview of systematic reviews on the pharmacoeconomic impact of the performance of pharmaceutical care in hospitals. Methods: A systematic search of the Cochrane Library databases, PubMed or MEDLINE, LILACS, Scopus, Web of Science, Google Scholar, and Open Thesis will be performed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The search will involve the use of keywords determined using the Medical Subject Headings database to define the search terms and include the following terms: ``pharmacoeconomics,'' ``pharmaceutical care,'' and ``hospital.'' The study designs to be included will be systematic reviews of good quality. Studies will be included that address pharmacoeconomics; studies that evaluated pharmaceutical care in hospitals; and studies published in Portuguese, English, or Spanish. The primary outcome sought in the systematic reviews will be the cost ratio in monetary units and the outcomes in monetary or natural units. The secondary economic outcomes considered will be determined based on factors associated with the drugs and translated into benefit, efficacy, or utility. Results: It is intended to start this overview in January 2023. Thus far, only previous searches have been carried out to contextualize the theme and build the protocol. Conclusions: This overview will determine the pharmacoeconomic impact of pharmaceutical care interventions in the hospital environment. In addition, this study will point out which clinical outcomes in natural units are impacted by the performance of pharmaceutical care and the strengths and limitations of each approach. It will also identify gaps in the literature and areas for future work. Trial Registration: PROSPERO CRD42019140665; https://tinyurl.com/bddwnz43 ", doi="10.2196/35865", url="https://www.researchprotocols.org/2023/1/e35865", url="http://www.ncbi.nlm.nih.gov/pubmed/37083592" } @Article{info:doi/10.2196/41100, author="Karapetian, Karina and Jeon, Min Soo and Kwon, Jin-Won and Suh, Young-Kyoon", title="Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus", journal="J Med Internet Res", year="2023", month="Mar", day="8", volume="25", pages="e41100", keywords="suicide", keywords="adverse drug events", keywords="information extraction", keywords="relation classification", keywords="bidirectional encoder representations from transformers", keywords="pharmacovigilance", keywords="natural language processing", keywords="PubMed", keywords="corpus", keywords="language model", abstract="Background: Drug-induced suicide has been debated as a crucial issue in both clinical and public health research. Published research articles contain valuable data on the drugs associated with suicidal adverse events. An automated process that extracts such information and rapidly detects drugs related to suicide risk is essential but has not been well established. Moreover, few data sets are available for training and validating classification models on drug-induced suicide. Objective: This study aimed to build a corpus of drug-suicide relations containing annotated entities for drugs, suicidal adverse events, and their relations. To confirm the effectiveness of the drug-suicide relation corpus, we evaluated the performance of a relation classification model using the corpus in conjunction with various embeddings. Methods: We collected the abstracts and titles of research articles associated with drugs and suicide from PubMed and manually annotated them along with their relations at the sentence level (adverse drug events, treatment, suicide means, or miscellaneous). To reduce the manual annotation effort, we preliminarily selected sentences with a pretrained zero-shot classifier or sentences containing only drug and suicide keywords. We trained a relation classification model using various Bidirectional Encoder Representations from Transformer embeddings with the proposed corpus. We then compared the performances of the model with different Bidirectional Encoder Representations from Transformer--based embeddings and selected the most suitable embedding for our corpus. Results: Our corpus comprised 11,894 sentences extracted from the titles and abstracts of the PubMed research articles. Each sentence was annotated with drug and suicide entities and the relationship between these 2 entities (adverse drug events, treatment, means, and miscellaneous). All of the tested relation classification models that were fine-tuned on the corpus accurately detected sentences of suicidal adverse events regardless of their pretrained type and data set properties. Conclusions: To our knowledge, this is the first and most extensive corpus of drug-suicide relations. ", doi="10.2196/41100", url="https://www.jmir.org/2023/1/e41100", url="http://www.ncbi.nlm.nih.gov/pubmed/36884281" } @Article{info:doi/10.2196/43529, author="Fossouo Tagne, Joel and Yakob, Amin Reginald and Mcdonald, Rachael and Wickramasinghe, Nilmini", title="Linking Activity Theory Within User-Centered Design: Novel Framework to Inform Design and Evaluation of Adverse Drug Reaction Reporting Systems in Pharmacy", journal="JMIR Hum Factors", year="2023", month="Feb", day="24", volume="10", pages="e43529", keywords="pharmacovigilance", keywords="adverse drug reaction", keywords="pharmacist", keywords="user-centered design", keywords="activity theory", abstract="Background: Adverse drug reactions (ADRs) may cause serious injuries including death. Timely reporting of ADRs may play a significant role in patient safety; however, underreporting exists. Enhancing the electronic communication of ADR information to regulators and between health care providers has the potential to reduce recurrent ADRs and improve patient safety. Objective: The main objectives were to explore the low rate of ADR reporting by community pharmacists (CPs) in Australia, evaluate the usability of an existing reporting system, and how this knowledge may influence the design of subsequent electronic ADR reporting systems. Methods: The study was carried out in 2 stages. Stage 1 involved qualitative semistructured interviews to identify CPs' perceived barriers and facilitators to ADR reporting. Data were analyzed by thematic analysis, and identified themes were subsequently aligned to the task-technology fit (TTF) framework. The second stage involved a usability evaluation of a commercial web-based ADR reporting system. A structured interview protocol that combined virtual observation, think-aloud moderating techniques, retrospective questioning of the overall user experience, and a System Usability Scale (SUS). The field notes from the interviews were subjected to thematic analysis. Results: In total, 12 CPs were interviewed in stage 1, and 7 CPs participated in stage 2. The interview findings show that CPs are willing to report ADRs but face barriers from environmental, organizational, and IT infrastructures. Increasing ADR awareness, improving workplace practices, and implementing user-focused electronic reporting systems were seen as facilitators of ADR reporting. User testing of an existing system resulted in above average usability (SUS 68.57); however, functional and user interpretation issues were identified. Design elements such as a drop-down menu, free-text entry, checkbox, and prefilled data fields were perceived to be extremely useful for navigating the system and facilitating ADR reporting. Conclusions: Existing reporting systems are not suited to report ADRs, or adapted to workflow, and are rarely used by CPs. Our study uncovered important contextual information for the design of future ADR reporting interventions. Based on our study, a multifaceted, theory-guided, user-centered, and best practice approach to design, implementation, and evaluation may be critical for the successful adoption of ADR reporting electronic interventions and patient safety. Future studies are needed to evaluate the effectiveness of theory-driven frameworks used in the design and implementation of ADR reporting systems. ", doi="10.2196/43529", url="https://humanfactors.jmir.org/2023/1/e43529", url="http://www.ncbi.nlm.nih.gov/pubmed/36826985" } @Article{info:doi/10.2196/40080, author="Fossouo Tagne, Joel and Yakob, Amin Reginald and Dang, Ha Thu and Mcdonald, Rachael and Wickramasinghe, Nilmini", title="Reporting, Monitoring, and Handling of Adverse Drug Reactions in Australia: Scoping Review", journal="JMIR Public Health Surveill", year="2023", month="Jan", day="16", volume="9", pages="e40080", keywords="pharmacovigilance", keywords="adverse drug reactions", keywords="primary care", keywords="digital health", abstract="Background: Adverse drug reactions (ADRs) are unintended consequences of medication use and may result in hospitalizations or deaths. Timely reporting of ADRs to regulators is essential for drug monitoring, research, and maintaining patient safety, but it has not been standardized in Australia. Objective: We sought to explore the ways that ADRs are monitored or reported in Australia. We reviewed how consumers and health care professionals participate in ADR monitoring and reporting. Methods: The Arksey and O'Malley framework provided a methodology to sort the data according to key themes and issues. Web of Science, Scopus, Embase, PubMed, CINAHL, and Computer \& Applied Sciences Complete databases were used to extract articles published from 2010 to 2021. Two reviewers screened the papers for eligibility, extracted key data, and provided descriptive analysis of the data. Results: Seven articles met the inclusion criteria. The Adverse Medicine Events Line (telephone reporting service) was introduced in 2003 to support consumer reporting of ADRs; however, only 10.4\% of consumers were aware of ADR reporting schemes. Consumers who experience side effects were more likely to report ADRs to their doctors or pharmacists than to the drug manufacturer. The documentation of ADR reports in hospital electronic health records showed that nurses and pharmacists were significantly less likely than doctors to omit the description of the drug reaction, and pharmacists were significantly more likely to enter the correct classification of the drug reaction than doctors. Review and analysis of all ADR reports submitted to the Therapeutic Goods Administration highlighted a decline in physician contribution from 28\% of ADR reporting in 2003 to 4\% in 2016; however, within this same time period, hospital and community pharmacists were a major source of ADR reporting (ie, 16\%). In 2014, there was an increase in ADR reporting by community pharmacists following the introduction of the GuildLink ADR web-based reporting system; however, a year later, the reporting levels dropped. In 2018, the Therapeutic Goods Administration introduced a black triangle scheme on the packaging of newly approved medicines, to remind and encourage ADR reporting on new medicines, but this was only marginally successful at increasing the quantity of ADR reports. Conclusions: Despite the existence of national and international guidelines for ADR reporting and management, there is substantial interinstitutional variability in the standards of ADR reporting among individual health care facilities. There is room for increased ADR reporting rates among consumers and health care professionals. A thorough assessment of the barriers and enablers to ADR reporting at the primary health care institutional levels is essential. Interventions to increase ADR reporting, for example, the black triangle scheme (alert or awareness) or GuildLink (digital health), have only had marginal effects and may benefit from further improvement revisions and awareness programs. ", doi="10.2196/40080", url="https://publichealth.jmir.org/2023/1/e40080", url="http://www.ncbi.nlm.nih.gov/pubmed/36645706" } @Article{info:doi/10.2196/41834, author="Mackey, Ken Tim and Jarmusch, K. Alan and Xu, Qing and Sun, Kunyang and Lu, Aileen and Aguirre, Shaden and Lim, Jessica and Bhakta, Simran and Dorrestein, C. Pieter", title="Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study", journal="JMIR Public Health Surveill", year="2022", month="Dec", day="23", volume="8", number="12", pages="e41834", keywords="online pharmacy", keywords="antimicrobial resistance", keywords="drug safety", keywords="cyberpharmacies", keywords="public health", keywords="health website", keywords="online health", keywords="web surveillance", keywords="patient safety", abstract="Background: Antimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription. Objective: The aim of this paper is to identify and characterize ``no prescription'' online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys. Methods: We first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants. Results: A total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants. Conclusions: Our study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients. ", doi="10.2196/41834", url="https://publichealth.jmir.org/2022/12/e41834", url="http://www.ncbi.nlm.nih.gov/pubmed/36563038" } @Article{info:doi/10.2196/24938, author="Lokala, Usha and Lamy, Francois and Daniulaityte, Raminta and Gaur, Manas and Gyrard, Amelie and Thirunarayan, Krishnaprasad and Kursuncu, Ugur and Sheth, Amit", title="Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study", journal="JMIR Public Health Surveill", year="2022", month="Dec", day="23", volume="8", number="12", pages="e24938", keywords="ontology", keywords="knowledge graph", keywords="semantic web", keywords="illicit drugs", keywords="cryptomarket", keywords="social media", abstract="Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports. Methods: The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. Results: The current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces. Conclusions: The DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research. ", doi="10.2196/24938", url="https://publichealth.jmir.org/2022/12/e24938", url="http://www.ncbi.nlm.nih.gov/pubmed/36563032" } @Article{info:doi/10.2196/36755, author="Dirkson, Anne and den Hollander, Dide and Verberne, Suzan and Desar, Ingrid and Husson, Olga and van der Graaf, A. Winette T. and Oosten, Astrid and Reyners, L. Anna K. and Steeghs, Neeltje and van Loon, Wouter and van Oortmerssen, Gerard and Gelderblom, Hans and Kraaij, Wessel", title="Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey Study", journal="JMIR Form Res", year="2022", month="Dec", day="15", volume="6", number="12", pages="e36755", keywords="social media", keywords="patient forum", keywords="sample bias", keywords="representativeness", keywords="pharmacovigilance", keywords="rare cancer", abstract="Background: Increasingly, social media is being recognized as a potential resource for patient-generated health data, for example, for pharmacovigilance. Although the representativeness of the web-based patient population is often noted as a concern, studies in this field are limited. Objective: This study aimed to investigate the sample bias of patient-centered social media in Dutch patients with gastrointestinal stromal tumor (GIST). Methods: A population-based survey was conducted in the Netherlands among 328 patients with GIST diagnosed 2-13 years ago to investigate their digital communication use with fellow patients. A logistic regression analysis was used to analyze clinical and demographic differences between forum users and nonusers. Results: Overall, 17.9\% (59/328) of survey respondents reported having contact with fellow patients via social media. Moreover, 78\% (46/59) of forum users made use of GIST patient forums. We found no statistically significant differences for age, sex, socioeconomic status, and time since diagnosis between forum users (n=46) and nonusers (n=273). Patient forum users did differ significantly in (self-reported) treatment phase from nonusers (P=.001). Of the 46 forum users, only 2 (4\%) were cured and not being monitored; 3 (7\%) were on adjuvant, curative treatment; 19 (41\%) were being monitored after adjuvant treatment; and 22 (48\%) were on palliative treatment. In contrast, of the 273 patients who did not use disease-specific forums to communicate with fellow patients, 56 (20.5\%) were cured and not being monitored, 31 (11.3\%) were on curative treatment, 139 (50.9\%) were being monitored after treatment, and 42 (15.3\%) were on palliative treatment. The odds of being on a patient forum were 2.8 times as high for a patient who is being monitored compared with a patient that is considered cured. The odds of being on a patient forum were 1.9 times as high for patients who were on curative (adjuvant) treatment and 10 times as high for patients who were in the palliative phase compared with patients who were considered cured. Forum users also reported a lower level of social functioning (84.8 out of 100) than nonusers (93.8 out of 100; P=.008). Conclusions: Forum users showed no particular bias on the most important demographic variables of age, sex, socioeconomic status, and time since diagnosis. This may reflect the narrowing digital divide. Overrepresentation and underrepresentation of patients with GIST in different treatment phases on social media should be taken into account when sourcing patient forums for patient-generated health data. A further investigation of the sample bias in other web-based patient populations is warranted. ", doi="10.2196/36755", url="https://formative.jmir.org/2022/12/e36755", url="http://www.ncbi.nlm.nih.gov/pubmed/36520526" } @Article{info:doi/10.2196/40597, author="Fossouo Tagne, Joel and Yakob, Amin Reginald and Mcdonald, Rachael and Wickramasinghe, Nilmini", title="Barriers and Facilitators Influencing Real-time and Digital-Based Reporting of Adverse Drug Reactions by Community Pharmacists: Qualitative Study Using the Task-Technology Fit Framework", journal="Interact J Med Res", year="2022", month="Oct", day="11", volume="11", number="2", pages="e40597", keywords="pharmacovigilance", keywords="adverse drug reaction", keywords="pharmacist", keywords="Task-Technology Fit", keywords="digital health", abstract="Background: Medication use can result in adverse drug reactions (ADRs) that cause increased morbidity and health care consumption for patients and could potentially be fatal. Timely reporting of ADRs to regulators may contribute to patient safety by facilitating information gathering on drug safety data. Currently, little is known about how community pharmacists (CPs) monitor, handle, and report ADRs in Australia. Objective: This study aimed to identify perceived barriers to and facilitators of ADR reporting by CPs in Australia and suggest digital interventions. Methods: A qualitative study with individual interviews was conducted with CPs working across Victoria, Australia, between April 2022 and May 2022. A semistructured interview guide was used to identify perceived barriers to and facilitators of ADR reporting among CPs. The data were analyzed using thematic analysis. We constructed themes from the CP-reported barriers and facilitators. The themes were subsequently aligned with the Task-Technology Fit framework. Results: A total of 12 CPs were interviewed. Identified barriers were lack of knowledge of both the ADR reporting process and ADR reporting systems, time constraints, lack of financial incentives, lack of organizational support for ADR reporting, inadequate IT systems, and preference to refer consumers to physicians. The proposed facilitators of ADR reporting included enhancing CPs knowledge and awareness of ADRs, financial incentives for ADR reporting, workflow-integrated ADR reporting technology systems, feedback provision to CPs on the reported ADRs, and promoting consumer ADR reporting. Conclusions: Barriers to and facilitators of ADR reporting spanned both the task and technology aspects of the Task-Technology Fit model. Addressing the identified barriers to ADR reporting and providing workplace technologies that support ADR reporting may improve ADR reporting by CPs. Further investigations to observe ADR handling and reporting within community pharmacies can enhance patient safety by increasing ADR reporting by CPs. ", doi="10.2196/40597", url="https://www.i-jmr.org/2022/2/e40597", url="http://www.ncbi.nlm.nih.gov/pubmed/36222800" } @Article{info:doi/10.2196/35464, author="Lee, Suehyun and Lee, Hoon Jeong and Kim, Juyun Grace and Kim, Jong-Yeup and Shin, Hyunah and Ko, Inseok and Choe, Seon and Kim, Han Ju", title="A Data-Driven Reference Standard for Adverse Drug Reaction (RS-ADR) Signal Assessment: Development and Validation", journal="J Med Internet Res", year="2022", month="Oct", day="6", volume="24", number="10", pages="e35464", keywords="adverse drug reaction", keywords="ADR", keywords="real-world data", keywords="RWD", keywords="real-world evidence", keywords="RWE", keywords="pharmacovigilance", keywords="PV", keywords="reference standard", keywords="pharmacology", keywords="drug reaction", abstract="Background: Pharmacovigilance using real-world data (RWD), such as multicenter electronic health records (EHRs), yields massively parallel adverse drug reaction (ADR) signals. However, proper validation of computationally detected ADR signals is not possible due to the lack of a reference standard for positive and negative associations. Objective: This study aimed to develop a reference standard for ADR (RS-ADR) to streamline the systematic detection, assessment, and understanding of almost all drug-ADR associations suggested by RWD analyses. Methods: We integrated well-known reference sets for drug-ADR pairs, including Side Effect Resource, Observational Medical Outcomes Partnership, and EU-ADR. We created a pharmacovigilance dictionary using controlled vocabularies and systematically annotated EHR data. Drug-ADR associations computed from MetaLAB and MetaNurse analyses of multicenter EHRs and extracted from the Food and Drug Administration Adverse Event Reporting System were integrated as ``empirically determined'' positive and negative reference sets by means of cross-validation between institutions. Results: The RS-ADR consisted of 1344 drugs, 4485 ADRs, and 6,027,840 drug-ADR pairs with positive and negative consensus votes as pharmacovigilance reference sets. After the curation of the initial version of RS-ADR, novel ADR signals such as ``famotidine--hepatic function abnormal'' were detected and reasonably validated by RS-ADR. Although the validation of the entire reference standard is challenging, especially with this initial version, the reference standard will improve as more RWD participate in the consensus voting with advanced pharmacovigilance dictionaries and analytic algorithms. One can check if a drug-ADR pair has been reported by our web-based search interface for RS-ADRs. Conclusions: RS-ADRs enriched with the pharmacovigilance dictionary, ADR knowledge, and real-world evidence from EHRs may streamline the systematic detection, evaluation, and causality assessment of computationally detected ADR signals. ", doi="10.2196/35464", url="https://www.jmir.org/2022/10/e35464", url="http://www.ncbi.nlm.nih.gov/pubmed/36201386" } @Article{info:doi/10.2196/38140, author="Yu, Deahan and Vydiswaran, Vinod V. G.", title="An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis", journal="JMIR Med Inform", year="2022", month="Sep", day="28", volume="10", number="9", pages="e38140", keywords="natural language processing", keywords="machine learning", keywords="adverse drug event", keywords="pharmacovigilance", keywords="social media", keywords="drug", keywords="clinical", keywords="public health", keywords="health monitoring", keywords="surveillance", keywords="drug effects", keywords="drug safety", abstract="Background: Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions. Objective: While using social media for collecting evidence of adverse drug events has potential, it is not clear whether social media are a reliable source for this information. Our work aims to (1) develop natural language processing approaches to identify adverse drug events on social media and (2) assess the reliability of social media data to identify adverse drug events. Methods: We propose a collocated long short-term memory network model with attentive pooling and aggregated, contextual representation generated by a pretrained model. We applied this model on large-scale Twitter data to identify adverse drug event--related tweets. We conducted a qualitative content analysis of these tweets to validate the reliability of social media data as a means to collect such information. Results: The model outperformed a variant without contextual representation during both the validation and evaluation phases. Through the content analysis of adverse drug event tweets, we observed that adverse drug event--related discussions had 7 themes. Mental health--related, sleep-related, and pain-related adverse drug event discussions were most frequent. We also contrast known adverse drug reactions to those mentioned in tweets. Conclusions: We observed a distinct improvement in the model when it used contextual information. However, our results reveal weak generalizability of the current systems to unseen data. Additional research is needed to fully utilize social media data and improve the robustness and reliability of natural language processing systems. The content analysis, on the other hand, showed that Twitter covered a sufficiently wide range of adverse drug events, as well as known adverse reactions, for the drugs mentioned in tweets. Our work demonstrates that social media can be a reliable data source for collecting adverse drug event mentions. ", doi="10.2196/38140", url="https://medinform.jmir.org/2022/9/e38140", url="http://www.ncbi.nlm.nih.gov/pubmed/36170004" } @Article{info:doi/10.2196/39229, author="Venkatakrishnan, Ajit and Chu, Brandon and Aggarwal, Pushkar", title="Photosensitivity From Avapritinib: Pharamacovigilance Analysis", journal="JMIR Dermatol", year="2022", month="Aug", day="10", volume="5", number="3", pages="e39229", keywords="oncology", keywords="Avapritinib", keywords="drug-induced", keywords="adverse reaction", keywords="photosensitizer", keywords="photosensitizing", keywords="cancer", keywords="pharmacovigilance", keywords="pharmaceutical", keywords="photosensitive", keywords="photosensitivity", keywords="light", keywords="adverse event", keywords="side effect", keywords="tumor", keywords="pharmacology", doi="10.2196/39229", url="https://derma.jmir.org/2022/3/e39229" } @Article{info:doi/10.2196/32543, author="Hussain, Zain and Sheikh, Zakariya and Tahir, Ahsen and Dashtipour, Kia and Gogate, Mandar and Sheikh, Aziz and Hussain, Amir", title="Artificial Intelligence--Enabled Social Media Analysis for Pharmacovigilance of COVID-19 Vaccinations in the United Kingdom: Observational Study", journal="JMIR Public Health Surveill", year="2022", month="May", day="27", volume="8", number="5", pages="e32543", keywords="COVID-19", keywords="artificial intelligence", keywords="deep learning", keywords="Facebook", keywords="health informatics", keywords="natural language processing", keywords="public health", keywords="sentiment analysis", keywords="social media", keywords="Twitter", keywords="infodemiology", keywords="vaccination", abstract="Background: ?The rollout of vaccines for COVID-19 in the United Kingdom started in December 2020. Uptake has been high, and there has been a subsequent reduction in infections, hospitalizations, and deaths among vaccinated individuals. However, vaccine hesitancy remains a concern, in particular relating to adverse effects following immunization (AEFIs). Social media analysis has the potential to inform policy makers about AEFIs being discussed by the public as well as public attitudes toward the national immunization campaign. Objective: ?We sought to assess the frequency and nature of AEFI-related mentions on social media in the United Kingdom and to provide insights on public sentiments toward COVID-19 vaccines. Methods: ?We extracted and analyzed over 121,406 relevant Twitter and Facebook posts, from December 8, 2020, to April 30, 2021. These were thematically filtered using a 2-step approach, initially using COVID-19--related keywords and then using vaccine- and manufacturer-related keywords. We identified AEFI-related keywords and modeled their word frequency to monitor their trends over 2-week periods. We also adapted and utilized our recently developed hybrid ensemble model, which combines state-of-the-art lexicon rule--based and deep learning--based approaches, to analyze sentiment trends relating to the main vaccines available in the United Kingdom. Results: ?Our COVID-19 AEFI search strategy identified 46,762 unique Facebook posts by 14,346 users and 74,644 tweets (excluding retweets) by 36,446 users over the 4-month period. We identified an increasing trend in the number of mentions for each AEFI on social media over the study period. The most frequent AEFI mentions were found to be symptoms related to appetite (n=79,132, 14\%), allergy (n=53,924, 9\%), injection site (n=56,152, 10\%), and clots (n=43,907, 8\%). We also found some rarely reported AEFIs such as Bell palsy (n=11,909, 2\%) and Guillain-Barre syndrome (n=9576, 2\%) being discussed as frequently as more well-known side effects like headache (n=10,641, 2\%), fever (n=12,707, 2\%), and diarrhea (n=16,559, 3\%). Overall, we found public sentiment toward vaccines and their manufacturers to be largely positive (58\%), with a near equal split between negative (22\%) and neutral (19\%) sentiments. The sentiment trend was relatively steady over time and had minor variations, likely based on political and regulatory announcements and debates. Conclusions: ?The most frequently discussed COVID-19 AEFIs on social media were found to be broadly consistent with those reported in the literature and by government pharmacovigilance. We also detected potential safety signals from our analysis that have been detected elsewhere and are currently being investigated. As such, we believe our findings support the use of social media analysis to provide a complementary data source to conventional knowledge sources being used for pharmacovigilance purposes. ", doi="10.2196/32543", url="https://publichealth.jmir.org/2022/5/e32543", url="http://www.ncbi.nlm.nih.gov/pubmed/35144240" } @Article{info:doi/10.2196/30426, author="Zheng, Chengyi and Duffy, Jonathan and Liu, Amy In-Lu and Sy, S. Lina and Navarro, A. Ronald and Kim, S. Sunhea and Ryan, S. Denison and Chen, Wansu and Qian, Lei and Mercado, Cheryl and Jacobsen, J. Steven", title="Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method", journal="JMIR Public Health Surveill", year="2022", month="May", day="24", volume="8", number="5", pages="e30426", keywords="health", keywords="informatics", keywords="shoulder injury related to vaccine administration", keywords="SIRVA", keywords="natural language processing", keywords="NLP", keywords="causal relation", keywords="temporal relation", keywords="pharmacovigilance", keywords="electronic health records", keywords="EHR", keywords="vaccine safety", keywords="artificial intelligence", keywords="big data", keywords="population health", keywords="real-world data", keywords="vaccines", abstract="Background: Shoulder injury related to vaccine administration (SIRVA) accounts for more than half of all claims received by the National Vaccine Injury Compensation Program. However, due to the difficulty of finding SIRVA cases in large health care databases, population-based studies are scarce. Objective: The goal of the research was to develop a natural language processing (NLP) method to identify SIRVA cases from clinical notes. Methods: We conducted the study among members of a large integrated health care organization who were vaccinated between April 1, 2016, and December 31, 2017, and had subsequent diagnosis codes indicative of shoulder injury. Based on a training data set with a chart review reference standard of 164 cases, we developed an NLP algorithm to extract shoulder disorder information, including prior vaccination, anatomic location, temporality and causality. The algorithm identified 3 groups of positive SIRVA cases (definite, probable, and possible) based on the strength of evidence. We compared NLP results to a chart review reference standard of 100 vaccinated cases. We then applied the final automated NLP algorithm to a broader cohort of vaccinated persons with a shoulder injury diagnosis code and performed manual chart confirmation on a random sample of NLP-identified definite cases and all NLP-identified probable and possible cases. Results: In the validation sample, the NLP algorithm had 100\% accuracy for identifying 4 SIRVA cases and 96 cases without SIRVA. In the broader cohort of 53,585 vaccinations, the NLP algorithm identified 291 definite, 124 probable, and 52 possible SIRVA cases. The chart-confirmation rates for these groups were 95.5\% (278/291), 67.7\% (84/124), and 17.3\% (9/52), respectively. Conclusions: The algorithm performed with high sensitivity and reasonable specificity in identifying positive SIRVA cases. The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation. ", doi="10.2196/30426", url="https://publichealth.jmir.org/2022/5/e30426", url="http://www.ncbi.nlm.nih.gov/pubmed/35608886" } @Article{info:doi/10.2196/32902, author="Tagwerker, Christian and Carias-Marines, Jane Mary and Smith, J. David", title="Effects of Pharmacogenomic Testing in Clinical Pain Management: Retrospective Study", journal="JMIRx Med", year="2022", month="May", day="3", volume="3", number="2", pages="e32902", keywords="pharmacogenomics", keywords="pain management", keywords="drug-drug interaction", keywords="DDI", keywords="pharmacy", keywords="prescriptions", keywords="genetics", keywords="genomics", keywords="drug-gene interaction", keywords="pain", abstract="Background: The availability of pharmacogenomic (PGx) methods to determine the right drug and dosage for individualized patient treatment has increased over the past decade. Adoption of the resulting PGx reports in a clinical setting and monitoring of clinical outcomes is a challenging and long-term commitment. Objective: This study summarizes an extended PGx deep sequencing panel intended for medication dosing and prescription guidance newly adopted in a pain management clinic. The primary outcome of this retrospective study reports the number of cases and types of drugs covered, for which PGx data appears to have assisted in optimal drug prescription and dosing. Methods: A PGx panel is described, encompassing 23 genes and 141 single-nucleotide polymorphisms or indels, combined with PGx dosing guidance and drug-gene interaction (DGI) and drug-drug interaction (DDI) reporting to prevent adverse drug reactions (ADRs). During a 2-year period, patients (N=171) were monitored in a pain management clinic. Urine toxicology, PGx reports, and progress notes were studied retrospectively for changes in prescription regimens before and after the PGx report was made available to the provider. An additional algorithm provided DGIs and DDIs to prevent ADRs. Results: Among patient PGx reports with medication lists provided (n=146), 57.5\% (n=84) showed one or more moderate and 5.5\% (n=8) at least one serious PGx interaction. A total of 96 (65.8\%) patients showed at least one moderate and 15.1\% (n=22) one or more serious DGIs or DDIs. A significant number of active changes in prescriptions based on the 102 PGx/DGI/DDI report results provided was observed for 85 (83.3\%) patients for which a specific drug was either discontinued or switched within the defined drug classes of the report, or a new drug was added. Conclusions: Preventative action was observed for all serious interactions, and only moderate interactions were tolerated for the lack of other alternatives. This study demonstrates the application of an extended PGx panel combined with a customized informational report to prevent ADRs and improve patient care. ", doi="10.2196/32902", url="https://med.jmirx.org/2022/2/e32902", url="http://www.ncbi.nlm.nih.gov/pubmed/37725552" } @Article{info:doi/10.2196/20168, author="Hatem, Reem and Nawaz, A. Faisal and Al-Sharif, A. Ghadah and Almoosa, Mohammad and Kattan, Wid and Tzivinikos, Christos and Amirali, Lila E. and Albanna, Ammar", title="Nonalcoholic Fatty Liver Disease in Children and Adolescents Taking Atypical Antipsychotic Medications: Protocol for a Systematic Review and Meta-analysis", journal="JMIR Res Protoc", year="2022", month="Mar", day="21", volume="11", number="3", pages="e20168", keywords="nonalcoholic fatty liver disease", keywords="psychopharmacology", keywords="antipsychotics", keywords="children", keywords="adolescents", keywords="overprescribing", keywords="pharmaceuticals", keywords="antipsychotic medications", keywords="medication", keywords="pediatric psychopharmacology", keywords="pharmacology", keywords="child and adolescent psychiatry", abstract="Background: Atypical antipsychotics (AAP) are commonly prescribed to children and adolescents and are associated with important adverse effects including weight gain and metabolic syndrome. Nonalcoholic fatty liver disease (NAFLD) is not only the most common pediatric liver disease but is also associated with serious complications including liver cirrhosis. Objective: Given that NAFLD and AAP are associated with metabolic syndrome, we aim to comprehensively examine the association between AAP and NAFLD in children and adolescents. Methods: We will conduct a systematic review of studies exploring NAFLD in subjects younger than 18 years on AAP published in English between 1950 and 2020 following the PRISMA (Preferred Reporting items for Systematic Reviews and Meta-Analysis) guidelines. Results: A PRISMA flowchart will be used present the study results after comprehensively reviewing studies on NAFLD in children and adolescents taking AAP. The first and second systematic searches will be conducted during December 2021. The results are expected to be published in June 2022. Conclusions: This research project will serve as a foundation for future studies and assist in devising interventions and reforming clinical guidelines for using AAP to ensure improved patient safety. International Registered Report Identifier (IRRID): PRR1-10.2196/20168 ", doi="10.2196/20168", url="https://www.researchprotocols.org/2022/3/e20168", url="http://www.ncbi.nlm.nih.gov/pubmed/35311689" } @Article{info:doi/10.2196/33873, author="Andrade, Q. Andre and Calabretto, Jean-Pierre and Pratt, L. Nicole and Kalisch-Ellett, M. Lisa and Kassie, M. Gizat and LeBlanc, T. Vanessa and Ramsay, Emmae and Roughead, E. Elizabeth", title="Implementation and Evaluation of a Digitally Enabled Precision Public Health Intervention to Reduce Inappropriate Gabapentinoid Prescription: Cluster Randomized Controlled Trial", journal="J Med Internet Res", year="2022", month="Jan", day="10", volume="24", number="1", pages="e33873", keywords="audit and feedback", keywords="digital health", keywords="precision public health", keywords="digital intervention", keywords="primary care", keywords="physician", keywords="health professional", keywords="health education", abstract="Background: Digital technologies can enable rapid targeted delivery of audit and feedback interventions at scale. Few studies have evaluated how mode of delivery affects clinical professional behavior change and none have assessed the feasibility of such an initiative at a national scale. Objective: The aim of this study was to develop and evaluate the effect of audit and feedback by digital versus postal (letter) mode of delivery on primary care physician behavior. Methods: This study was developed as part of the Veterans' Medicines Advice and Therapeutics Education Services (MATES) program, an intervention funded by the Australian Government Department of Veterans' Affairs that provides targeted education and patient-specific audit with feedback to Australian general practitioners, as well as educational material to veterans and other health professionals. We performed a cluster randomized controlled trial of a multifaceted intervention to reduce inappropriate gabapentinoid prescription, comparing digital and postal mode of delivery. All veteran patients targeted also received an educational intervention (postal delivery). Efficacy was measured using a linear mixed-effects model as the average number of gabapentinoid prescriptions standardized by defined daily dose (individual level), and number of veterans visiting a psychologist in the 6 and 12 months following the intervention. Results: The trial involved 2552 general practitioners in Australia and took place in March 2020. Both intervention groups had a significant reduction in total gabapentinoid prescription by the end of the study period (digital: mean reduction of 11.2\%, P=.004; postal: mean reduction of 11.2\%, P=.001). We found no difference between digital and postal mode of delivery in reduction of gabapentinoid prescriptions at 12 months (digital: --0.058, postal: --0.058, P=.98). Digital delivery increased initiations to psychologists at 12 months (digital: 3.8\%, postal: 2.0\%, P=.02). Conclusions: Our digitally delivered professional behavior change intervention was feasible, had comparable effectiveness to the postal intervention with regard to changes in medicine use, and had increased effectiveness with regard to referrals to a psychologist. Given the logistical benefits of digital delivery in nationwide programs, the results encourage exploration of this mode in future interventions. ", doi="10.2196/33873", url="https://www.jmir.org/2022/1/e33873", url="http://www.ncbi.nlm.nih.gov/pubmed/35006086" } @Article{info:doi/10.2196/33311, author="Park, Susan and Choi, Hyun So and Song, Yun-Kyoung and Kwon, Jin-Won", title="Comparison of Online Patient Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study", journal="JMIR Public Health Surveill", year="2022", month="Jan", day="4", volume="8", number="1", pages="e33311", keywords="drug safety", keywords="pharmacovigilance", keywords="tramadol", keywords="social media", keywords="adverse effect", abstract="Background: Tramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. Objective: We aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. Methods: This study used 2 data sets, 1 from patients' drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. Results: From the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satis?ed all 3 signal detection criteria. In addition, 20 serious AEs were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients' symptom descriptions, tramadol-induced pain might also be an unexpected AE. Conclusions: This study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. ", doi="10.2196/33311", url="https://publichealth.jmir.org/2022/1/e33311", url="http://www.ncbi.nlm.nih.gov/pubmed/34982723" } @Article{info:doi/10.2196/28632, author="Chopard, Daphne and Treder, S. Matthias and Corcoran, Padraig and Ahmed, Nagheen and Johnson, Claire and Busse, Monica and Spasic, Irena", title="Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach", journal="JMIR Med Inform", year="2021", month="Dec", day="24", volume="9", number="12", pages="e28632", keywords="natural language processing", keywords="deep learning", keywords="machine learning", keywords="classification", abstract="Background: Pharmacovigilance and safety reporting, which involve processes for monitoring the use of medicines in clinical trials, play a critical role in the identification of previously unrecognized adverse events or changes in the patterns of adverse events. Objective: This study aims to demonstrate the feasibility of automating the coding of adverse events described in the narrative section of the serious adverse event report forms to enable statistical analysis of the aforementioned patterns. Methods: We used the Uni?ed Medical Language System (UMLS) as the coding scheme, which integrates 217 source vocabularies, thus enabling coding against other relevant terminologies such as the International Classification of Diseases--10th Revision, Medical Dictionary for Regulatory Activities, and Systematized Nomenclature of Medicine). We used MetaMap, a highly configurable dictionary lookup software, to identify the mentions of the UMLS concepts. We trained a binary classifier using Bidirectional Encoder Representations from Transformers (BERT), a transformer-based language model that captures contextual relationships, to differentiate between mentions of the UMLS concepts that represented adverse events and those that did not. Results: The model achieved a high F1 score of 0.8080, despite the class imbalance. This is 10.15 percent points lower than human-like performance but also 17.45 percent points higher than that of the baseline approach. Conclusions: These results confirmed that automated coding of adverse events described in the narrative section of serious adverse event reports is feasible. Once coded, adverse events can be statistically analyzed so that any correlations with the trialed medicines can be estimated in a timely fashion. ", doi="10.2196/28632", url="https://medinform.jmir.org/2021/12/e28632", url="http://www.ncbi.nlm.nih.gov/pubmed/34951601" } @Article{info:doi/10.2196/31321, author="Passardi, Alessandro and Serra, Patrizia and Donati, Caterina and Fiori, Federica and Prati, Sabrina and Vespignani, Roberto and Taglioni, Gabriele and Farfaneti Ghetti, Patrizia and Martinelli, Giovanni and Nanni, Oriana and Altini, Mattia and Frassineti, Luca Giovanni and Minguzzi, Vittoria Martina", title="An Integrated Model to Improve Medication Reconciliation in Oncology: Prospective Interventional Study", journal="J Med Internet Res", year="2021", month="Dec", day="20", volume="23", number="12", pages="e31321", keywords="medication recognition", keywords="medication reconciliation", keywords="IT platform", keywords="community pharmacies", keywords="healthcare transitions", keywords="pharmacy", keywords="oncology", keywords="drug incompatibility", keywords="information technology", keywords="drug interactions", abstract="Background: Accurate medication reconciliation reduces the risk of drug incompatibilities and adverse events that can occur during transitions in care. Community pharmacies (CPs) are a crucial part of the health care system and could be involved in collecting essential information on conventional and supplementary drugs used at home. Objective: The aim of this paper was to establish an alliance between our cancer institute, Istituto Romagnolo per lo Studio dei Tumori (IRST), and CPs, the latter entrusted with the completion of a pharmacological recognition survey. We also aimed to integrate the national information technology (IT) platform of CPs with the electronic medical records of IRST. Methods: Cancer patients undergoing antiblastic treatments were invited to select a CP taking part in the study and to complete the pharmacological recognition step. The information collected by the pharmacist was sent to the electronic medical records of IRST through the new IT platform, after which the oncologist performed the reconciliation process. Results: A total of 66 CPs completed surveys for 134 patients. An average of 5.9 drugs per patient was used at home, with 12 or more used in the most advanced age groups. Moreover, 60\% (80/134) of the patients used nonconventional products or critical foods. Some potential interactions between nonconventional medications and cancer treatments were reported. Conclusions: In the PROF-1 (Progetto di Rete in Oncologia con le Farmacie di comunit{\`a} della Romagna) study, an alliance was created between our cancer center and CPs to improve medication reconciliation, and a new integrated IT platform was validated. Trial Registration: ClinicalTrials.gov NCT04796142; https://clinicaltrials.gov/ct2/show/NCT04796142 ", doi="10.2196/31321", url="https://www.jmir.org/2021/12/e31321", url="http://www.ncbi.nlm.nih.gov/pubmed/34932001" } @Article{info:doi/10.2196/29286, author="Bannay, Aur{\'e}lie and Bories, Mathilde and Le Corre, Pascal and Riou, Christine and Lemordant, Pierre and Van Hille, Pascal and Chazard, Emmanuel and Dode, Xavier and Cuggia, Marc and Bouzill{\'e}, Guillaume", title="Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case", journal="JMIR Med Inform", year="2021", month="Dec", day="13", volume="9", number="12", pages="e29286", keywords="drug interactions", keywords="statins", keywords="administrative claims", keywords="health care", keywords="big data", keywords="data linking", keywords="data warehousing", abstract="Background: Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrep{\^o}t H{\^o}pital), and a data set extracted from the French national claim data warehouse (Syst{\`e}me National des Donn{\'e}es de Sant{\'e} [SNDS]). Objective: This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. Methods: A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. Results: The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74\% and 97.07\% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45\% and 3253/14,675, 22.17\%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. Conclusions: This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data. ", doi="10.2196/29286", url="https://medinform.jmir.org/2021/12/e29286", url="http://www.ncbi.nlm.nih.gov/pubmed/34898457" } @Article{info:doi/10.2196/27188, author="Chan, Erina and Small, S. Serena and Wickham, E. Maeve and Cheng, Vicki and Balka, Ellen and Hohl, M. Corinne", title="The Utility of Different Data Standards to Document Adverse Drug Event Symptoms and Diagnoses: Mixed Methods Study", journal="J Med Internet Res", year="2021", month="Dec", day="10", volume="23", number="12", pages="e27188", keywords="adverse drug events", keywords="health information technology", keywords="data standards", abstract="Background: Existing systems to document adverse drug events often use free text data entry, which produces nonstandardized and unstructured data that are prone to misinterpretation. Standardized terminology may improve data quality; however, it is unclear which data standard is most appropriate for documenting adverse drug event symptoms and diagnoses. Objective: This study aims to compare the utility, strengths, and weaknesses of different data standards for documenting adverse drug event symptoms and diagnoses. Methods: We performed a mixed methods substudy of a multicenter retrospective chart review. We reviewed the research records of prospectively diagnosed adverse drug events at 5 Canadian hospitals. A total of 2 pharmacy research assistants independently entered the symptoms and diagnoses for the adverse drug events using four standards: Medical Dictionary for Regulatory Activities (MedDRA), Systematized Nomenclature of Medicine (SNOMED) Clinical Terms, SNOMED Adverse Reaction (SNOMED ADR), and International Classification of Diseases (ICD) 11th Revision. Disagreements between research assistants regarding the case-specific utility of data standards were discussed until a consensus was reached. We used consensus ratings to determine the proportion of adverse drug events covered by a data standard and coded and analyzed field notes from the consensus sessions. Results: We reviewed 573 adverse drug events and found that MedDRA and ICD-11 had excellent coverage of adverse drug event symptoms and diagnoses. MedDRA had the highest number of matches between the research assistants, whereas ICD-11 had the fewest. SNOMED ADR had the lowest proportion of adverse drug event coverage. The research assistants were most likely to encounter terminological challenges with SNOMED ADR and usability challenges with ICD-11, whereas least likely to encounter challenges with MedDRA. Conclusions: Usability, comprehensiveness, and accuracy are important features of data standards for documenting adverse drug event symptoms and diagnoses. On the basis of our results, we recommend the use of MedDRA. ", doi="10.2196/27188", url="https://www.jmir.org/2021/12/e27188", url="http://www.ncbi.nlm.nih.gov/pubmed/34890351" } @Article{info:doi/10.2196/26407, author="Wu, Hong and Ji, Jiatong and Tian, Haimei and Chen, Yao and Ge, Weihong and Zhang, Haixia and Yu, Feng and Zou, Jianjun and Nakamura, Mitsuhiro and Liao, Jun", title="Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding--Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model", journal="JMIR Med Inform", year="2021", month="Dec", day="1", volume="9", number="12", pages="e26407", keywords="deep learning", keywords="BERT", keywords="adverse drug reaction", keywords="named entity recognition", keywords="electronic medical records", abstract="Background: With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective: This study describes how to identify ADR-related information from Chinese ADE reports. Methods: Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results: The NER model achieved relatively high performance, with a precision of 96.4\%, recall of 96.0\%, and F1 score of 96.2\%. This indicates that the performance of the BBC-Radical model (precision 87.2\%, recall 85.7\%, and F1 score 86.4\%) is much better than that of the manual method (precision 86.1\%, recall 73.8\%, and F1 score 79.5\%) in the recognition task of each kind of entity. Conclusions: The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation. ", doi="10.2196/26407", url="https://medinform.jmir.org/2021/12/e26407", url="http://www.ncbi.nlm.nih.gov/pubmed/34855616" } @Article{info:doi/10.2196/28763, author="Teramoto, Kei and Takeda, Toshihiro and Mihara, Naoki and Shimai, Yoshie and Manabe, Shirou and Kuwata, Shigeki and Kondoh, Hiroshi and Matsumura, Yasushi", title="Detecting Adverse Drug Events Through the Chronological Relationship Between the Medication Period and the Presence of Adverse Reactions From Electronic Medical Record Systems: Observational Study", journal="JMIR Med Inform", year="2021", month="Nov", day="1", volume="9", number="11", pages="e28763", keywords="real world data", keywords="electronic medical record", keywords="adverse drug event", abstract="Background: Medicines may cause various adverse reactions. An enormous amount of money and effort is spent investigating adverse drug events (ADEs) in clinical trials and postmarketing surveillance. Real-world data from multiple electronic medical records (EMRs) can make it easy to understand the ADEs that occur in actual patients. Objective: In this study, we generated a patient medication history database from physician orders recorded in EMRs, which allowed the period of medication to be clearly identified. Methods: We developed a method for detecting ADEs based on the chronological relationship between the presence of an adverse event and the medication period. To verify our method, we detected ADEs with alanine aminotransferase elevation in patients receiving aspirin, clopidogrel, and ticlopidine. The accuracy of the detection was evaluated with a chart review and by comparison with the Roussel Uclaf Causality Assessment Method (RUCAM), which is a standard method for detecting drug-induced liver injury. Results: The calculated rates of ADE with ALT elevation in patients receiving aspirin, clopidogrel, and ticlopidine were 3.33\% (868/26,059 patients), 3.70\% (188/5076 patients), and 5.69\% (226/3974 patients), respectively, which were in line with the rates of previous reports. We reviewed the medical records of the patients in whom ADEs were detected. Our method accurately predicted ADEs in 90\% (27/30patients) treated with aspirin, 100\% (9/9 patients) treated with clopidogrel, and 100\% (4/4 patients) treated with ticlopidine. Only 3 ADEs that were detected by the RUCAM were not detected by our method. Conclusions: These findings demonstrate that the present method is effective for detecting ADEs based on EMR data. ", doi="10.2196/28763", url="https://medinform.jmir.org/2021/11/e28763", url="http://www.ncbi.nlm.nih.gov/pubmed/33993103" } @Article{info:doi/10.2196/24336, author="Alvarez-Mon, Angel Miguel and Llavero-Valero, Maria and Asunsolo del Barco, Angel and Zaragoz{\'a}, Cristina and Ortega, A. Miguel and Lahera, Guillermo and Quintero, Javier and Alvarez-Mon, Melchor", title="Areas of Interest and Attitudes Toward Antiobesity Drugs: Thematic and Quantitative Analysis Using Twitter", journal="J Med Internet Res", year="2021", month="Oct", day="26", volume="23", number="10", pages="e24336", keywords="obesity", keywords="social media", keywords="Twitter", keywords="drug therapy", keywords="pharmacotherapy", keywords="attitude", keywords="thematic analysis", keywords="quantitative analysis", keywords="drug", abstract="Background: Antiobesity drugs are prescribed for the treatment of obesity in conjunction with healthy eating, physical activity, and behavior modification. However, poor adherence rates have been reported. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. The analysis of tweets has become a tool for health research. Objective: The aim of this study is to investigate the content and key metrics of tweets referring to antiobesity drugs. Methods: In this observational quantitative and qualitative study, we focused on tweets containing hashtags related to antiobesity drugs between September 20, 2019, and October 31, 2019. Tweets were first classified according to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. We additionally rated it as positive or negative. Furthermore, we classified any links included within a tweet as either scientific or nonscientific. Finally, the number of retweets generated as well as the dissemination and sentiment score obtained by the antiobesity drugs analyzed were also measured. Results: We analyzed a total of 2045 tweets, 945 of which were excluded according to the criteria of the study. Finally, 320 out of the 1,100 remaining tweets were also excluded because their content, although related to drugs for obesity treatment, did not address the efficacy, side effects, or adherence to medication. Liraglutide and semaglutide accumulated the majority of tweets (682/780, 87.4\%). Notably, the content that generated the highest frequency of tweets was related to treatment efficacy, with liraglutide-, semaglutide-, and lorcaserin-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to liraglutide and semaglutide. Semaglutide-related tweets obtained the highest probability of likes and were the most disseminated within the Twitter community. Conclusions: This analysis of posted tweets related to antiobesity drugs shows that the interest, beliefs, and experiences regarding these pharmacological treatments are heterogeneous. The efficacy of the treatment accounts for the majority of interest among Twitter users. ", doi="10.2196/24336", url="https://www.jmir.org/2021/10/e24336", url="http://www.ncbi.nlm.nih.gov/pubmed/34698653" } @Article{info:doi/10.2196/32730, author="Dasgupta, Soham and Jayagopal, Aishwarya and Jun Hong, Lim Abel and Mariappan, Ragunathan and Rajan, Vaibhav", title="Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation", journal="JMIR Med Inform", year="2021", month="Oct", day="25", volume="9", number="10", pages="e32730", keywords="adverse drug event", keywords="knowledge graph", keywords="Embedding of Semantic Predications", keywords="biomedical literature", abstract="Background: Adverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilance, is routinely conducted to find unknown ADEs. A wealth of information, which facilitates ADE discovery, lies in the growing body of biomedical literature. Knowledge graphs (KGs) encode information from the literature, where the vertices and the edges represent clinical concepts and their relations, respectively. The scale and unstructured form of the literature necessitates the use of natural language processing (NLP) to automatically create such KGs. Previous studies have demonstrated the utility of such literature-derived KGs in ADE prediction. Through unsupervised learning of the representations (features) of clinical concepts from the KG, which are used in machine learning models, state-of-the-art results for ADE prediction were obtained on benchmark data sets. Objective: Due to the use of NLP to infer literature-derived KGs, there is noise in the form of false positive (erroneous) and false negative (absent) nodes and edges. Previous representation learning methods do not account for such inaccuracies in the graph. NLP algorithms can quantify the confidence in their inference of extracted concepts and relations from the literature. Our hypothesis, which motivates this work, is that by using such confidence scores during representation learning, the learned embeddings would yield better features for ADE prediction models. Methods: We developed methods to use these confidence scores on two well-known representation learning methods---DeepWalk and Translating Embeddings for Modeling Multi-relational Data (TransE)---to develop their weighted versions: Weighted DeepWalk and Weighted TransE. These methods were used to learn representations from a large literature-derived KG, the Semantic MEDLINE Database, which contains more than 93 million clinical relations. They were compared with Embedding of Semantic Predications, which, to our knowledge, is the best reported representation learning method using the Semantic MEDLINE Database with state-of-the-art results for ADE prediction. Representations learned from different methods were used (separately) as features of drugs and diseases to build classification models for ADE prediction using benchmark data sets. The methods were compared rigorously over multiple cross-validation settings. Results: The weighted versions we designed were able to learn representations that yielded more accurate predictive models than the corresponding unweighted versions of both DeepWalk and TransE, as well as Embedding of Semantic Predications, in our experiments. There were performance improvements of up to 5.75\% in the F1-score and 8.4\% in the area under the receiver operating characteristic curve value, thus advancing the state of the art in ADE prediction from literature-derived KGs. Conclusions: Our classification models can be used to aid pharmacovigilance teams in detecting potentially new ADEs. Our experiments demonstrate the importance of modeling inaccuracies in the inferred KGs for representation learning. ", doi="10.2196/32730", url="https://medinform.jmir.org/2021/10/e32730", url="http://www.ncbi.nlm.nih.gov/pubmed/34694230" } @Article{info:doi/10.2196/27714, author="Lavertu, Adam and Hamamsy, Tymor and Altman, B. Russ", title="Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis", journal="J Med Internet Res", year="2021", month="Oct", day="21", volume="23", number="10", pages="e27714", keywords="social media for health", keywords="pharmacovigilance", keywords="adverse drug reactions", keywords="machine learning", keywords="network analysis", keywords="word embeddings", keywords="drug safety", keywords="social media", abstract="Background: Adverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts. Objective: The aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates. Methods: We present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities. Results: Our Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and ?0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect. Conclusions: Our SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data. ", doi="10.2196/27714", url="https://www.jmir.org/2021/10/e27714", url="http://www.ncbi.nlm.nih.gov/pubmed/34673524" } @Article{info:doi/10.2196/29238, author="Matsuda, Shinichi and Ohtomo, Takumi and Tomizawa, Shiho and Miyano, Yuki and Mogi, Miwako and Kuriki, Hiroshi and Nakayama, Terumi and Watanabe, Shinichi", title="Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="29", volume="7", number="6", pages="e29238", keywords="social media", keywords="adverse drug reaction", keywords="pharmacovigilance", keywords="text mining", keywords="systemic lupus erythematosus", keywords="natural language processing", keywords="NLP", keywords="lupus", keywords="chronic disease", keywords="narrative", keywords="insurance", keywords="data", keywords="epidemiology", keywords="burden", keywords="Japan", keywords="patient-generated", abstract="Background: Gaining insights that cannot be obtained from health care databases from patients has become an important topic in pharmacovigilance. Objective: Our objective was to demonstrate a use case, in which patient-generated data were incorporated in pharmacovigilance, to understand the epidemiology and burden of illness in Japanese patients with systemic lupus erythematosus. Methods: We used data on systemic lupus erythematosus, an autoimmune disease that substantially impairs quality of life, from 2 independent data sets. To understand the disease's epidemiology, we analyzed a Japanese health insurance claims database. To understand the disease's burden, we analyzed text data collected from Japanese disease blogs (t?by?ki) written by patients with systemic lupus erythematosus. Natural language processing was applied to these texts to identify frequent patient-level complaints, and term frequency--inverse document frequency was used to explore patient burden during treatment. We explored health-related quality of life based on patient descriptions. Results: We analyzed data from 4694 and 635 patients with systemic lupus erythematosus in the health insurance claims database and t?by?ki blogs, respectively. Based on health insurance claims data, the prevalence of systemic lupus erythematosus is 107.70 per 100,000 persons. T?by?ki text data analysis showed that pain-related words (eg, pain, severe pain, arthralgia) became more important after starting treatment. We also found an increase in patients' references to mobility and self-care over time, which indicated increased attention to physical disability due to disease progression. Conclusions: A classical medical database represents only a part of a patient's entire treatment experience, and analysis using solely such a database cannot represent patient-level symptoms or patient concerns about treatments. This study showed that analysis of t?by?ki blogs can provide added information on patient-level details, advancing patient-centric pharmacovigilance. ", doi="10.2196/29238", url="https://publichealth.jmir.org/2021/6/e29238", url="http://www.ncbi.nlm.nih.gov/pubmed/34255719" } @Article{info:doi/10.2196/30137, author="Lee, Jae-Young and Lee, Yae-Seul and Kim, Hyun Dong and Lee, Sol Han and Yang, Ram Bo and Kim, Gyu Myeong", title="The Use of Social Media in Detecting Drug Safety--Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping Review", journal="JMIR Public Health Surveill", year="2021", month="Jun", day="28", volume="7", number="6", pages="e30137", keywords="adverse event", keywords="black box warning", keywords="detect", keywords="pharmacovigilance", keywords="real-world data", keywords="review", keywords="safety", keywords="social media", keywords="withdrawal of approval", abstract="Background: Social media has become a new source for obtaining real-world data on adverse drug reactions. Many studies have investigated the use of social media to detect early signals of adverse drug reactions. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory study with a positive control (eg, new black box warnings, labeling changes, or withdrawals) is required. Objective: This study aimed to evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance. Methods: This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. A researcher searched PubMed and EMBASE in January 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected, and the results of the studies were summarized. Results: A total of 14 studies were included in this scoping review. Most studies (8/14, 57.1\%\%) collected data from a single source, and 10 (71.4\%) used specialized health care social networks and forums. The analytical methods used in these studies varied considerably. Three studies (21.4\%) manually annotated posts, while 5 (35.7\%) adopted machine learning algorithms. Nine studies (64.2\%) concluded that social media could detect signals 3 months to 9 years before action from regulatory authorities. Most of these studies (8/9, 88.9\%) were conducted on specialized health care social networks and forums. On the contrary, 5 (35.7\%) studies yielded modest or negative results. Of these, 2 (40\%) used generic social networking sites, 2 (40\%) used specialized health care networks and forums, and 1 (20\%) used both generic social networking sites and specialized health care social networks and forums. The most recently published study recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing. Conclusions: Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized health care social networks and forums. Further studies are required to advance natural language processing and mine real-world data on social media. ", doi="10.2196/30137", url="https://publichealth.jmir.org/2021/6/e30137", url="http://www.ncbi.nlm.nih.gov/pubmed/34185021" } @Article{info:doi/10.2196/26262, author="Shen, Chunying and Jiang, Bin and Yang, Qilian and Wang, Chengnan and Lu, Z. Kevin and Gu, Meng and Yuan, Jing", title="Mobile Apps for Drug--Drug Interaction Checks in Chinese App Stores: Systematic Review and Content Analysis", journal="JMIR Mhealth Uhealth", year="2021", month="Jun", day="15", volume="9", number="6", pages="e26262", keywords="drug interaction", keywords="MARS", keywords="app", keywords="drug safety", keywords="drugs", keywords="mHealth", abstract="Background: As a computerized drug--drug interaction (DDI) alert system has not been widely implemented in China, health care providers are relying on mobile health (mHealth) apps as references for checking drug information, including DDIs. Objective: The main objective of this study was to evaluate the quality and content of mHealth apps supporting DDI checking in Chinese app stores. Methods: A systematic review was carried out in November 2020 to identify mHealth apps providing DDI checking in both Chinese iOS and Android platforms. We extracted the apps' general information (including the developer, operating system, costs, release date, size, number of downloads, and average rating), scientific or clinical basis, and accountability, based on a multidimensional framework for evaluation of apps. The quality of mHealth apps was evaluated by using the Mobile App Rating Scale (MARS). Descriptive statistics, including numbers and percentages, were calculated to describe the characteristics of the apps. For each app selected for evaluation, the section-specific MARS scores were calculated by taking the arithmetic mean, while the overall MARS score was described as the arithmetic mean of the section scores. In addition, the Cohen kappa ($\kappa$) statistic was used to evaluate the interrater agreement. Results: A total of 7 apps met the selection criteria, and only 3 included citations. The average rating score for Android apps was 3.5, with a minimum of 1.0 and a maximum of 4.9, while the average rating score for iOS apps was 4.7, with a minimum of 4.2 and a maximum of 4.9. The mean MARS score was 3.69 out of 5 (95\% CI 3.34-4.04), with the lowest score of 1.96 for Medication Guidelines and the highest score of 4.27 for MCDEX mobile. The greatest variation was observed in the information section, which ranged from 1.41 to 4.60. The functionality section showed the highest mean score of 4.05 (95\% CI 3.71-4.40), whereas the engagement section resulted in the lowest average score of 3.16 (95\% CI 2.81-3.51). For the information quality section, which was the focus of this analysis, the average score was 3.42, with the MCDEX mobile app having the highest score of 4.6 and the Medication Guidelines app having the lowest score of 1.9. For the overall MARS score, the Cohen interrater $\kappa$ was 0.354 (95\% CI 0.236-0.473), the Fleiss $\kappa$ was 0.353 (95\% CI, 0.234-0.472), and the Krippendorff $\alpha$ was 0.356 (95\% CI 0.237-0.475). Conclusions: This study systematically reviewed the mHealth apps in China with a DDI check feature. The majority of investigated apps demonstrated high quality with accurate and comprehensive information on DDIs. However, a few of the apps that had a massive number of downloads in the Chinese market provided incorrect information. Given these apps might be used by health care providers for checking potential DDIs, this creates a substantial threat to patient safety. ", doi="10.2196/26262", url="https://mhealth.jmir.org/2021/6/e26262", url="http://www.ncbi.nlm.nih.gov/pubmed/33962910" } @Article{info:doi/10.2196/28616, author="Milne-Ives, Madison and Lam, Ching and Rehman, Najib and Sharif, Raja and Meinert, Edward", title="Distributed Ledger Infrastructure to Verify Adverse Event Reporting (DeLIVER): Proposal for a Proof-of-Concept Study", journal="JMIR Res Protoc", year="2021", month="Jun", day="10", volume="10", number="6", pages="e28616", keywords="adverse drug reaction reporting systems", keywords="drug-related side effects and adverse reactions", keywords="blockchain", keywords="mobile applications", keywords="distributed ledger technology", abstract="Background: Adverse drug event reporting is critical for ensuring patient safety; however, numbers of reports have been declining. There is a need for a more user-friendly reporting system and for a means of verifying reports that have been filed. Objective: This project has 2 main objectives: (1) to identify the perceived benefits and barriers in the current reporting of adverse events by patients and health care providers and (2) to develop a distributed ledger infrastructure and user interface to collect and collate adverse event reports to create a comprehensive and interoperable database. Methods: A review of the literature will be conducted to identify the strengths and limitations of the current UK adverse event reporting system (the Yellow Card System). If insufficient information is found in this review, a survey will be created to collect data from system users. The results of these investigations will be incorporated into the development of a mobile and web app for adverse event reporting. A digital infrastructure will be built using distributed ledger technology to provide a means of linking reports with existing pharmaceutical tracking systems. Results: The key outputs of this project will be the development of a digital infrastructure, including a backend distributed ledger system and an app-based user interface. Conclusions: This infrastructure is expected to improve the accuracy and efficiency of adverse event reporting systems by enabling the monitoring of specific medicines or medical devices over their life course while protecting patients' personal health data. International Registered Report Identifier (IRRID): PRR1-10.2196/28616 ", doi="10.2196/28616", url="https://www.researchprotocols.org/2021/6/e28616", url="http://www.ncbi.nlm.nih.gov/pubmed/34110292" } @Article{info:doi/10.2196/26589, author="Saha, Koustuv and Torous, John and Kiciman, Emre and De Choudhury, Munmun", title="Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data", journal="JMIR Ment Health", year="2021", month="Mar", day="19", volume="8", number="3", pages="e26589", keywords="antidepressants", keywords="symptoms", keywords="side effects", keywords="digital pharmacovigilance", keywords="social media", keywords="mental health", keywords="linguistic markers", keywords="digital health", abstract="Background: Antidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants. Objective: We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media. Methods: On a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018. Results: Five major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects. Conclusions: This work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants. ", doi="10.2196/26589", url="https://mental.jmir.org/2021/3/e26589", url="http://www.ncbi.nlm.nih.gov/pubmed/33739296" } @Article{info:doi/10.2196/19266, author="Zhou, Zeyun and Hultgren, Emerson Kyle", title="Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis", journal="JMIR Public Health Surveill", year="2020", month="Sep", day="30", volume="6", number="3", pages="e19266", keywords="adverse drug reactions", keywords="FAERS", keywords="social media reporting", keywords="pharmacovigilance", abstract="Background: Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations. Objective: The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents. Methods: ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data. Results: A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter's Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively). Conclusions: FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases. ", doi="10.2196/19266", url="https://publichealth.jmir.org/2020/3/e19266", url="http://www.ncbi.nlm.nih.gov/pubmed/32996889" } @Article{info:doi/10.2196/20007, author="Michelson, Matthew and Chow, Tiffany and Martin, A. Neil and Ross, Mike and Tee Qiao Ying, Amelia and Minton, Steven", title="Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine", journal="J Med Internet Res", year="2020", month="Aug", day="17", volume="22", number="8", pages="e20007", keywords="meta-analysis", keywords="rapid meta-analysis", keywords="artificial intelligence", keywords="drug", keywords="analysis", keywords="hydroxychloroquine", keywords="toxic", keywords="COVID-19", keywords="treatment", keywords="side effect", keywords="ocular", keywords="eye", abstract="Background: Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. Objective: We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. Methods: The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. Results: By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4\% (95\% CI 1.11\%-9.96\%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. Conclusions: We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis. ", doi="10.2196/20007", url="http://www.jmir.org/2020/8/e20007/", url="http://www.ncbi.nlm.nih.gov/pubmed/32804086" } @Article{info:doi/10.2196/18758, author="Jung, Young Se and Hwang, Hee and Lee, Keehyuck and Lee, Ho-Young and Kim, Eunhye and Kim, Miyoung and Cho, Young In", title="Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis", journal="JMIR Med Inform", year="2020", month="Jul", day="22", volume="8", number="7", pages="e18758", keywords="clinical decision support system", keywords="electronic health record", keywords="medication safety", keywords="Computerized Provider Order Entry (CPOE)", abstract="Background: Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective: In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators. Methods: A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation. Results: Quantitative analysis revealed that physicians' expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability. Conclusions: This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future. ", doi="10.2196/18758", url="https://medinform.jmir.org/2020/7/e18758", url="http://www.ncbi.nlm.nih.gov/pubmed/32706717" } @Article{info:doi/10.2196/18417, author="Dandala, Bharath and Joopudi, Venkata and Tsou, Ching-Huei and Liang, J. Jennifer and Suryanarayanan, Parthasarathy", title="Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models", journal="JMIR Med Inform", year="2020", month="Jul", day="10", volume="8", number="7", pages="e18417", keywords="electronic health records", keywords="adverse drug events", keywords="natural language processing", keywords="deep learning", keywords="information extraction", keywords="adverse drug reaction reporting systems", keywords="named entity recognition", keywords="relation extraction", abstract="Background: An adverse drug event (ADE) is commonly defined as ``an injury resulting from medical intervention related to a drug.'' Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient's ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes. Objective: This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients. Methods: This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning--based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient. Results: Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug?reason (F1=0.650 versus F1=0.579) and drug?ADE (F1=0.490 versus F1=0.476) relations. Conclusions: This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning--based concepts and relation extraction. This study demonstrates the potential for deep learning--based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance. ", doi="10.2196/18417", url="https://medinform.jmir.org/2020/7/e18417", url="http://www.ncbi.nlm.nih.gov/pubmed/32459650" } @Article{info:doi/10.2196/17073, author="Black, C. Joshua and Margolin, R. Zachary and Olson, A. Richard and Dart, C. Richard", title="Online Conversation Monitoring to Understand the Opioid Epidemic: Epidemiological Surveillance Study", journal="JMIR Public Health Surveill", year="2020", month="Jun", day="29", volume="6", number="2", pages="e17073", keywords="epidemiological surveillance", keywords="infoveillance", keywords="infodemiology", keywords="opioids", keywords="social media", keywords="misuse", keywords="abuse", keywords="addiction", keywords="overdose", keywords="death", abstract="Background: Between 2016 and 2017, the national mortality rate involving opioids continued its escalation; opioid deaths rose from 42,249 to 47,600, bringing the public health crisis to a new height. Considering that 69\% of adults in the United States use online social media sites, a resource that builds a more complete understanding of prescription drug misuse and abuse could supplement traditional surveillance instruments. The Food and Drug Administration has identified 5 key risks and consequences of opioid drugs---misuse, abuse, addiction, overdose, and death. Identifying posts that discuss these key risks could lead to novel information that is not typically captured by traditional surveillance systems. Objective: The goal of this study was to describe the trends of online posts (frequency over time) involving abuse, misuse, addiction, overdose, and death in the United States and to describe the types of websites that host these discussions. Internet posts that mentioned fentanyl, hydrocodone, oxycodone, or oxymorphone were examined. Methods: Posts that did not refer to personal experiences were removed, after which 3.1 million posts remained. A stratified sample of 61,000 was selected. Unstructured data were classified into 5 key risks by manually coding for key outcomes of misuse, abuse, addiction, overdose, and death. Sampling probabilities of the coded posts were used to estimate the total post volume for each key risk. Results: Addiction and misuse were the two most commonly discussed key risks for hydrocodone, oxycodone, and oxymorphone. For fentanyl, overdose and death were the most discussed key risks. Fentanyl had the highest estimated number of misuse-, overdose-, and death-related mentions (41,808, 42,659, and 94,169, respectively). Oxycodone had the highest estimated number of abuse- and addiction-related mentions (3548 and 12,679, respectively). The estimated volume of online posts for fentanyl increased by more than 10-fold in late 2017 and 2018. The odds of discussing fentanyl overdose (odds ratios [OR] 4.32, 95\% CI 2.43-7.66) and death (OR 5.05, 95\% CI 3.10-8.21) were higher for social media, while the odds of discussing fentanyl abuse (OR 0.10, 95\% CI 0.04-0.22) and addiction (OR 0.24, 95\% CI 0.15-0.38) were higher for blogs and forums. Conclusions: Of the 5 FDA-defined key risks, fentanyl overdose and death has dominated discussion in recent years, while discussion of oxycodone, hydrocodone, and oxymorphone has decreased. As drug-related deaths continue to increase, an understanding of the motivations, circumstances, and consequences of drug abuse would assist in developing policy responses. Furthermore, content was notably different based on media origin, and studies that exclusively use either social media sites (such as Twitter) or blogs and forums could miss important content. This study sets out sustainable, ongoing methodology for surveilling internet postings regarding these drugs. ", doi="10.2196/17073", url="http://publichealth.jmir.org/2020/2/e17073/", url="http://www.ncbi.nlm.nih.gov/pubmed/32597786" } @Article{info:doi/10.2196/17353, author="Yu, Yue and Ruddy, Kathryn and Mansfield, Aaron and Zong, Nansu and Wen, Andrew and Tsuji, Shintaro and Huang, Ming and Liu, Hongfang and Shah, Nilay and Jiang, Guoqian", title="Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study", journal="JMIR Med Inform", year="2020", month="Jun", day="12", volume="8", number="6", pages="e17353", keywords="immunotherapy/adverse effects", keywords="drug-related side effects and adverse reactions", keywords="pharmacovigilance", keywords="adverse drug reaction reporting systems/standards", keywords="text mining", abstract="Background: Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective: The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration--approved immune checkpoint inhibitors. Methods: In our framework, we first used the Food and Drug Administration's Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. Results: By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56\%) were labeled signals, 10 (11\%) were unlabeled published signals, and 31 (33\%) were potentially new signals. Conclusions: We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection. ", doi="10.2196/17353", url="http://medinform.jmir.org/2020/6/e17353/", url="http://www.ncbi.nlm.nih.gov/pubmed/32530430" } @Article{info:doi/10.2196/15861, author="O'Connor, Karen and Sarker, Abeed and Perrone, Jeanmarie and Gonzalez Hernandez, Graciela", title="Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines", journal="J Med Internet Res", year="2020", month="Feb", day="26", volume="22", number="2", pages="e15861", keywords="prescription drug misuse", keywords="social media", keywords="substance abuse detection", keywords="natural language processing", keywords="machine learning", keywords="infodemiology", keywords="infoveillance", abstract="Background: Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective: This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse--related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods: We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes---abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results: Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00\% (95\% CI 71.4-74.5) over the test set (n=3271). Conclusions: Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks. ", doi="10.2196/15861", url="http://www.jmir.org/2020/2/e15861/", url="http://www.ncbi.nlm.nih.gov/pubmed/32130117" } @Article{info:doi/10.2196/14809, author="Timimi, Farris and Ray, Sara and Jones, Erik and Aase, Lee and Hoffman, Kathleen", title="Patient-Reported Outcomes in Online Communications on Statins, Memory, and Cognition: Qualitative Analysis Using Online Communities", journal="J Med Internet Res", year="2019", month="Nov", day="28", volume="21", number="11", pages="e14809", keywords="social media", keywords="hydroxymethylglutaryl-CoA reductase inhibitors", keywords="drug-related side effects and adverse reactions", keywords="memory loss", keywords="PROMs", keywords="pharmacovigilance", keywords="infodemiology", keywords="infoveillance", keywords="peer-support groups", abstract="Background: In drug development clinical trials, there is a need for balance between restricting variables by setting eligibility criteria and representing the broader patient population that may use a product once it is approved. Similarly, although recent policy initiatives focusing on the inclusion of historically underrepresented groups are being implemented, barriers still remain. These limitations of clinical trials may mask potential product benefits and side effects. To bridge these gaps, online communication in health communities may serve as an additional population signal for drug side effects. Objective: The aim of this study was to employ a nontraditional dataset to identify drug side-effect signals. The study was designed to apply both natural language processing (NLP) technology and hands-on linguistic analysis to a set of online posts from known statin users to (1) identify any underlying crossover between the use of statins and impairment of memory or cognition and (2) obtain patient lexicon in their descriptions of experiences with statin medications and memory changes. Methods: Researchers utilized user-generated content on Inspire, looking at over 11 million posts across Inspire. Posts were written by patients and caregivers belonging to a variety of communities on Inspire. After identifying these posts, researchers used NLP and hands-on linguistic analysis to draw and expand upon correlations among statin use, memory, and cognition. Results: NLP analysis of posts identified statistical correlations between statin users and the discussion of memory impairment, which were not observed in control groups. NLP found that, out of all members on Inspire, 3.1\% had posted about memory or cognition. In a control group of those who had posted about TNF inhibitors, 6.2\% had also posted about memory and cognition. In comparison, of all those who had posted about a statin medication, 22.6\% (P<.001) also posted about memory and cognition. Furthermore, linguistic analysis of a sample of posts provided themes and context to these statistical findings. By looking at posts from statin users about memory, four key themes were found and described in detail in the data: memory loss, aphasia, cognitive impairment, and emotional change. Conclusions: Correlations from this study point to a need for further research on the impact of statins on memory and cognition. Furthermore, when using nontraditional datasets, such as online communities, NLP and linguistic methodologies broaden the population for identifying side-effect signals. For side effects such as those on memory and cognition, where self-reporting may be unreliable, these methods can provide another avenue to inform patients, providers, and the Food and Drug Administration. ", doi="10.2196/14809", url="http://www.jmir.org/2019/11/e14809/", url="http://www.ncbi.nlm.nih.gov/pubmed/31778117" } @Article{info:doi/10.2196/13371, author="Borchert, S. Jill and Wang, Bo and Ramzanali, Muzaina and Stein, B. Amy and Malaiyandi, M. Latha and Dineley, E. Kirk", title="Adverse Events Due to Insomnia Drugs Reported in a Regulatory Database and Online Patient Reviews: Comparative Study", journal="J Med Internet Res", year="2019", month="Nov", day="8", volume="21", number="11", pages="e13371", keywords="drug safety", keywords="drug ineffective", keywords="postmarketing", keywords="pharmacovigilance", keywords="internet", keywords="pharmacoepidemiology", keywords="adverse effect", keywords="hypnotic", keywords="insomnia", keywords="patient-reported outcomes", abstract="Background: Patient online drug reviews are a resource for other patients seeking information about the practical benefits and drawbacks of drug therapies. Patient reviews may also serve as a source of postmarketing safety data that are more user-friendly than regulatory databases. However, the reliability of online reviews has been questioned, because they do not undergo professional review and lack means of verification. Objective: We evaluated online reviews of hypnotic medications, because they are commonly used and their therapeutic efficacy is particularly amenable to patient self-evaluation. Our primary objective was to compare the types and frequencies of adverse events reported to the Food and Drug Administration Adverse Event Reporting System (FAERS) with analogous information in patient reviews on the consumer health website Drugs.com. The secondary objectives were to describe patient reports of efficacy and adverse events and assess the influence of medication cost, effectiveness, and adverse events on user ratings of hypnotic medications. Methods: Patient ratings and narratives were retrieved from 1407 reviews on Drugs.com between February 2007 and March 2018 for eszopiclone, ramelteon, suvorexant, zaleplon, and zolpidem. Reviews were coded to preferred terms in the Medical Dictionary for Regulatory Activities. These reviews were compared to 5916 cases in the FAERS database from January 2015 to September 2017. Results: Similar adverse events were reported to both Drugs.com and FAERS. Both resources identified a lack of efficacy as a common complaint for all five drugs. Both resources revealed that amnesia commonly occurs with eszopiclone, zaleplon, and zolpidem, while nightmares commonly occur with suvorexant. Compared to FAERS, online reviews of zolpidem reported a much higher frequency of amnesia and partial sleep activities. User ratings were highest for zolpidem and lowest for suvorexant. Statistical analyses showed that patient ratings are influenced by considerations of efficacy and adverse events, while drug cost is unimportant. Conclusions: For hypnotic medications, online patient reviews and FAERS emphasized similar adverse events. Online reviewers rated drugs based on perception of efficacy and adverse events. We conclude that online patient reviews of hypnotics are a valid source that can supplement traditional adverse event reporting systems. ", doi="10.2196/13371", url="http://www.jmir.org/2019/11/e13371/", url="http://www.ncbi.nlm.nih.gov/pubmed/31702558" } @Article{info:doi/10.2196/15830, author="Black, Curtis Joshua and Rockhill, Karilynn and Forber, Alyssa and Amioka, Elise and May, Patrick K. and Haynes, M. Colleen and Dasgupta, Nabarun and Dart, C. Richard", title="An Online Survey for Pharmacoepidemiological Investigation (Survey of Non-Medical Use of Prescription Drugs Program): Validation Study", journal="J Med Internet Res", year="2019", month="Oct", day="25", volume="21", number="10", pages="e15830", keywords="nonprobability methods", keywords="general population survey", keywords="drug abuse", keywords="calibration weights", abstract="Background: In rapidly changing fields such as the study of drug use, the need for accurate and timely data is paramount to properly inform policy and intervention decisions. Trends in drug use can change rapidly by month, and using study designs with flexible modules could present advantages. Timely data from online panels can inform proactive interventions against emerging trends, leading to a faster public response. However, threats to validity from using online panels must be addressed to create accurate estimates. Objective: The objective of this study was to demonstrate a comprehensive methodological approach that optimizes a nonprobability, online opt-in sample to provide timely, accurate national estimates on prevalence of drug use. Methods: The Survey of Non-Medical Use of Prescription Drugs Program from the Researched Abuse, Diversion and Addiction Related Surveillance (RADARS) System is an online, cross-sectional survey on drug use in the United States, and several best practices were implemented. To optimize final estimates, two best practices were investigated in detail: exclusion of respondents showing careless or improbable responding patterns and calibration of weights. The approach in this work was to cumulatively implement each method, which improved key estimates during the third quarter 2018 survey launch. Cutoffs for five exclusion criteria were tested. Using a series of benchmarks, average relative bias and changes in bias were calculated for 33 different weighting variable combinations. Results: There were 148,274 invitations sent to panelists, with 40,021 who initiated the survey (26.99\%). After eligibility assessment, 20.23\% (29,998/148,274) of the completed questionnaires were available for analysis. A total of 0.52\% (157/29,998) of respondents were excluded based on careless or improbable responses; however, these exclusions had larger impacts on lower volume drugs. Number of exclusions applied were negatively correlated to total dispensing volume by drug (Spearman $\rho$=--.88, P<.001). A weighting scheme including three demographic and two health characteristics reduced average relative bias by 31.2\%. After weighting, estimates of drug use decreased, reflecting a weighted sample that had healthier benchmarks than the unweighted sample. Conclusions: Our study illustrates a new approach to using nonprobability online panels to achieve national prevalence estimates for drug abuse. We were able to overcome challenges with using nonprobability internet samples, including misclassification due to improbable responses. Final drug use and health estimates demonstrated concurrent validity to national probability-based drug use and health surveys. Inclusion of multiple best practices cumulatively improved the estimates generated. This method can bridge the information gap when there is a need for prompt, accurate national data. ", doi="10.2196/15830", url="http://www.jmir.org/2019/10/e15830/", url="http://www.ncbi.nlm.nih.gov/pubmed/31654568" } @Article{info:doi/10.2196/14791, author="Munnoch, Sally-Anne and Cashman, Patrick and Peel, Roseanne and Attia, John and Hure, Alexis and Durrheim, N. David", title="Participant-Centered Online Active Surveillance for Adverse Events Following Vaccination in a Large Clinical Trial: Feasibility and Usability Study", journal="J Med Internet Res", year="2019", month="Oct", day="23", volume="21", number="10", pages="e14791", keywords="clinical trials", keywords="active surveillance", keywords="adverse events following immunization", keywords="technology", keywords="vaccination", abstract="Background: Active participant monitoring of adverse events following immunization (AEFI) is a recent development to improve the speed and transparency of vaccine safety postmarketing. Vaxtracker, an online tool used to monitor vaccine safety, has successfully demonstrated its usefulness in postmarketing surveillance of newly introduced childhood vaccines. However, its use in older participants, or for monitoring patients participating in large clinical trials, has not been evaluated. Objective: The objective of this study was to monitor AEFIs in older participants enrolled in the Australian Study for the Prevention through the Immunisation of Cardiovascular Events (AUSPICE) trial, and to evaluate the usefulness and effectiveness of Vaxtracker in this research setting. Methods: AUSPICE is a multicenter, randomized, placebo-controlled, double-blinded trial in which participants aged 55 to 61 years were given either the pneumococcal polysaccharide vaccine (23vPPV) or 0.9\% saline placebo. Vaxtracker was used to monitor AEFIs in participants in either treatment arm through the administration of two online questionnaires. A link to each questionnaire was sent to participants via email or short message service (SMS) text message 7 and 28 days following vaccination. Data were collated and analyzed in near-real time to identify any possible safety signals indicating problems with the vaccine or placebo. Results: All 4725 AUSPICE participants were enrolled in Vaxtracker. Participant response rates for the first and final survey were 96.47\% (n=4558) and 96.65\% (n=4525), respectively. The online survey was completed by 90.23\% (4083/4525) of Vaxtracker participants within 3 days of receiving the link. AEFIs were reported by 34.40\% (805/2340) of 23vPPV recipients and 10.29\% (240/2332) of placebo recipients in the 7 days following vaccination. Dominant symptoms for vaccine and placebo recipients were pain at the injection site (587/2340, 25.09\%) and fatigue (103/2332, 4.42\%), respectively. Females were more likely to report symptoms following vaccination with 23vPPV compared with males (433/1138, 38.05\% versus 372/1202, 30.95\%; P<.001). Conclusions: Vaxtracker is an effective tool for monitoring AEFIs in the 55 to 61 years age group. Participant response rates were high for both surveys, in both treatment arms and for each method of sending the survey. This study indicates that administration of 23vPPV was well-tolerated in this cohort. Vaxtracker has successfully demonstrated its application in the monitoring of adverse events in near-real time following vaccination in people participating in a national clinical trial. Trial Registration: Australian New Zealand Trial Registry Number (ACTRN) 12615000536561; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368506 ", doi="10.2196/14791", url="https://www.jmir.org/2019/10/e14791", url="http://www.ncbi.nlm.nih.gov/pubmed/31647470" } @Article{info:doi/10.2196/jmir.7081, author="Golder, Su and Scantlebury, Arabella and Christmas, Helen", title="Understanding Public Attitudes Toward Researchers Using Social Media for Detecting and Monitoring Adverse Events Data: Multi Methods Study", journal="J Med Internet Res", year="2019", month="Aug", day="29", volume="21", number="8", pages="e7081", keywords="adverse effects", keywords="social media", keywords="ethics", keywords="research", keywords="qualitative research", keywords="digital health", keywords="infodemiology", keywords="infoveillance", keywords="pharmacovigilance", keywords="surveillance", abstract="Background: Adverse events are underreported in research studies, particularly randomized controlled trials and pharmacovigilance studies. A method that researchers could use to identify more complete safety profiles for medications is to use social media analytics. However, patient's perspectives on the ethical issues associated with using patient reports of adverse drug events on social media are unclear. Objective: The objective of this study was to explore the ethics of using social media for detecting and monitoring adverse events for research purposes using a multi methods approach. Methods: A multi methods design comprising qualitative semistructured interviews (n=24), a focus group (n=3), and 3 Web-based discussions (n=20) with members of the public was adopted. Findings from a recent systematic review on the use of social media for monitoring adverse events provided a theoretical framework to interpret the study's findings. Results: Views were ascertained regarding the potential benefits and harms of the research, privacy expectations, informed consent, and social media platform. Although the majority of participants were supportive of social media content being used for research on adverse events, a small number of participants strongly opposed the idea. The potential benefit of the research was cited as the most influential factor to whether participants would give their consent to their data being used for research. There were also some caveats to people's support for the use of their social media data for research purposes: the type of social media platform and consideration of the vulnerability of the social media user. Informed consent was regarded as difficult to obtain and this divided the opinion on whether it should be sought. Conclusions: Social media users were generally positive about their social media data being used for research purposes; particularly for research on adverse events. However, approval was dependent on the potential benefit of the research and that individuals are protected from harm. Further study is required to establish when consent is required for an individual's social media data to be used. ", doi="10.2196/jmir.7081", url="http://www.jmir.org/2019/8/e7081/", url="http://www.ncbi.nlm.nih.gov/pubmed/31469079" } @Article{info:doi/10.2196/13003, author="Rezaallah, Bita and Lewis, John David and Pierce, Carrie and Zeilhofer, Hans-Florian and Berg, Britt-Isabelle", title="Social Media Surveillance of Multiple Sclerosis Medications Used During Pregnancy and Breastfeeding: Content Analysis", journal="J Med Internet Res", year="2019", month="Aug", day="07", volume="21", number="8", pages="e13003", keywords="pharmacovigilance", keywords="machine learning", keywords="pregnancy outcome", keywords="postpartum", keywords="central nervous system agents", keywords="risk assessment", keywords="text mining", abstract="Background: Multiple sclerosis (MS) is a chronic neurological disease occurring mostly in women of childbearing age. Pregnant women with MS are usually excluded from clinical trials; as users of the internet, however, they are actively engaged in threads and forums on social media. Social media provides the potential to explore real-world patient experiences and concerns about the use of medicinal products during pregnancy and breastfeeding. Objective: This study aimed to analyze the content of posts concerning pregnancy and use of medicines in online forums; thus, the study aimed to gain a thorough understanding of patients' experiences with MS medication. Methods: Using the names of medicinal products as search terms, we collected posts from 21 publicly available pregnancy forums, which were accessed between March 2015 and March 2018. After the identification of relevant posts, we analyzed the content of each post using a content analysis technique and categorized the main topics that users discussed most frequently. Results: We identified 6 main topics in 70 social media posts. These topics were as follows: (1) expressing personal experiences with MS medication use during the reproductive period (55/70, 80\%), (2) seeking and sharing advice about the use of medicines (52/70, 74\%), (3) progression of MS during and after pregnancy (35/70, 50\%), (4) discussing concerns about MS medications during the reproductive period (35/70, 50\%), (5) querying the possibility of breastfeeding while taking MS medications (30/70, 42\%), and (6) commenting on communications with physicians (26/70, 37\%). Conclusions: Overall, many pregnant women or women considering pregnancy shared profound uncertainties and specific concerns about taking medicines during the reproductive period. There is a significant need to provide advice and guidance to MS patients concerning the use of medicines in pregnancy and postpartum as well as during breastfeeding. Advice must be tailored to the circumstances of each patient and, of course, to the individual medicine. Information must be provided by a trusted source with relevant expertise and made publicly available. ", doi="10.2196/13003", url="https://www.jmir.org/2019/8/e13003/", url="http://www.ncbi.nlm.nih.gov/pubmed/31392963" } @Article{info:doi/10.2196/11264, author="Nikfarjam, Azadeh and Ransohoff, D. Julia and Callahan, Alison and Jones, Erik and Loew, Brian and Kwong, Y. Bernice and Sarin, Y. Kavita and Shah, H. Nigam", title="Early Detection of Adverse Drug Reactions in Social Health Networks: A Natural Language Processing Pipeline for Signal Detection", journal="JMIR Public Health Surveill", year="2019", month="Jun", day="03", volume="5", number="2", pages="e11264", keywords="natural language processing", keywords="signal detection", keywords="adverse drug reactions", keywords="social media", keywords="drug-related side effects", keywords="medical oncology", keywords="antineoplastic agents", keywords="machine learning", abstract="Background: Adverse drug reactions (ADRs) occur in nearly all patients on chemotherapy, causing morbidity and therapy disruptions. Detection of such ADRs is limited in clinical trials, which are underpowered to detect rare events. Early recognition of ADRs in the postmarketing phase could substantially reduce morbidity and decrease societal costs. Internet community health forums provide a mechanism for individuals to discuss real-time health concerns and can enable computational detection of ADRs. Objective: The goal of this study is to identify cutaneous ADR signals in social health networks and compare the frequency and timing of these ADRs to clinical reports in the literature. Methods: We present a natural language processing-based, ADR signal-generation pipeline based on patient posts on Internet social health networks. We identified user posts from the Inspire health forums related to two chemotherapy classes: erlotinib, an epidermal growth factor receptor inhibitor, and nivolumab and pembrolizumab, immune checkpoint inhibitors. We extracted mentions of ADRs from unstructured content of patient posts. We then performed population-level association analyses and time-to-detection analyses. Results: Our system detected cutaneous ADRs from patient reports with high precision (0.90) and at frequencies comparable to those documented in the literature but an average of 7 months ahead of their literature reporting. Known ADRs were associated with higher proportional reporting ratios compared to negative controls, demonstrating the robustness of our analyses. Our named entity recognition system achieved a 0.738 microaveraged F-measure in detecting ADR entities, not limited to cutaneous ADRs, in health forum posts. Additionally, we discovered the novel ADR of hypohidrosis reported by 23 patients in erlotinib-related posts; this ADR was absent from 15 years of literature on this medication and we recently reported the finding in a clinical oncology journal. Conclusions: Several hundred million patients report health concerns in social health networks, yet this information is markedly underutilized for pharmacosurveillance. We demonstrated the ability of a natural language processing-based signal-generation pipeline to accurately detect patient reports of ADRs months in advance of literature reporting and the robustness of statistical analyses to validate system detections. Our findings suggest the important contributions that social health network data can play in contributing to more comprehensive and timely pharmacovigilance. ", doi="10.2196/11264", url="http://publichealth.jmir.org/2019/2/e11264/", url="http://www.ncbi.nlm.nih.gov/pubmed/31162134" } @Article{info:doi/10.2196/11448, author="Arnoux-Guenegou, Armelle and Girardeau, Yannick and Chen, Xiaoyi and Deldossi, Myrtille and Aboukhamis, Rim and Faviez, Carole and Dahamna, Badisse and Karapetiantz, Pierre and Guillemin-Lanne, Sylvie and Lillo-Le Lou{\"e}t, Agn{\`e}s and Texier, Nathalie and Burgun, Anita and Katsahian, Sandrine", title="The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard", journal="JMIR Res Protoc", year="2019", month="May", day="07", volume="8", number="5", pages="e11448", keywords="social media", keywords="drug-related side effects and adverse reactions", keywords="natural language processing", keywords="data mining", keywords="MedDRA", keywords="Racine Pharma", abstract="Background: Social media is a potential source of information on postmarketing drug safety surveillance that still remains unexploited nowadays. Information technology solutions aiming at extracting adverse reactions (ADRs) from posts on health forums require a rigorous evaluation methodology if their results are to be used to make decisions. First, a gold standard, consisting of manual annotations of the ADR by human experts from the corpus extracted from social media, must be implemented and its quality must be assessed. Second, as for clinical research protocols, the sample size must rely on statistical arguments. Finally, the extraction methods must target the relation between the drug and the disease (which might be either treated or caused by the drug) rather than simple co-occurrences in the posts. Objective: We propose a standardized protocol for the evaluation of a software extracting ADRs from the messages on health forums. The study is conducted as part of the Adverse Drug Reactions from Patient Reports in Social Media project. Methods: Messages from French health forums were extracted. Entity recognition was based on Racine Pharma lexicon for drugs and Medical Dictionary for Regulatory Activities terminology for potential adverse events (AEs). Natural language processing--based techniques automated the ADR information extraction (relation between the drug and AE entities). The corpus of evaluation was a random sample of the messages containing drugs and/or AE concepts corresponding to recent pharmacovigilance alerts. A total of 2 persons experienced in medical terminology manually annotated the corpus, thus creating the gold standard, according to an annotator guideline. We will evaluate our tool against the gold standard with recall, precision, and f-measure. Interannotator agreement, reflecting gold standard quality, will be evaluated with hierarchical kappa. Granularities in the terminologies will be further explored. Results: Necessary and sufficient sample size was calculated to ensure statistical confidence in the assessed results. As we expected a global recall of 0.5, we needed at least 384 identified ADR concepts to obtain a 95\% CI with a total width of 0.10 around 0.5. The automated ADR information extraction in the corpus for evaluation is already finished. The 2 annotators already completed the annotation process. The analysis of the performance of the ADR information extraction module as compared with gold standard is ongoing. Conclusions: This protocol is based on the standardized statistical methods from clinical research to create the corpus, thus ensuring the necessary statistical power of the assessed results. Such evaluation methodology is required to make the ADR information extraction software useful for postmarketing drug safety surveillance. International Registered Report Identifier (IRRID): RR1-10.2196/11448 ", doi="10.2196/11448", url="http://www.researchprotocols.org/2019/5/e11448/", url="http://www.ncbi.nlm.nih.gov/pubmed/31066711" } @Article{info:doi/10.2196/11016, author="Wang, Chi-Shiang and Lin, Pei-Ju and Cheng, Ching-Lan and Tai, Shu-Hua and Kao Yang, Yea-Huei and Chiang, Jung-Hsien", title="Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model", journal="J Med Internet Res", year="2019", month="Feb", day="06", volume="21", number="2", pages="e11016", keywords="adverse drug reactions", keywords="deep neural network", keywords="drug representation", keywords="machine learning", keywords="pharmacovigilance", abstract="Background: Adverse drug reactions (ADRs) are common and are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance. Objective: The objective of this study was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN). Methods: We designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfill two main purposes: identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships using the word-embedding approach to process substantial biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset. Results: Using the drug information and the ADRs reported up to 2009, we predicted the ADRs of drugs recorded up to 2012. There were 746 drugs and 232 new drugs, which were only recorded in 2012 with 1325 ADRs. The experimental results showed that the overall performance of our model with mean average precision at top-10 achieved is 0.523 and the rea under the receiver operating characteristic curve (AUC) score achieved is 0.844 for ADR prediction on the dataset. Conclusions: Our model is effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past. ", doi="10.2196/11016", url="http://www.jmir.org/2019/2/e11016/", url="http://www.ncbi.nlm.nih.gov/pubmed/30724742" } @Article{info:doi/10.2196/jmir.9284, author="Lancaster, Karla and Abuzour, Aseel and Khaira, Manmeet and Mathers, Annalise and Chan, April and Bui, Vivian and Lok, Annie and Thabane, Lehana and Dolovich, Lisa", title="The Use and Effects of Electronic Health Tools for Patient Self-Monitoring and Reporting of Outcomes Following Medication Use: Systematic Review", journal="J Med Internet Res", year="2018", month="Dec", day="18", volume="20", number="12", pages="e294", keywords="eHealth", keywords="mHealth", keywords="electronic health record", keywords="telemedicine", keywords="self-report", keywords="patient portals", keywords="patient-centered care", keywords="drug monitoring", keywords="adverse effects", abstract="Background: Electronic health (eHealth) tools are becoming increasingly popular for helping patients' self-manage chronic conditions. Little research, however, has examined the effect of patients using eHealth tools to self-report their medication management and use. Similarly, there is little evidence showing how eHealth tools might prompt patients and health care providers to make appropriate changes to medication use. Objective: The objective of this systematic review was to determine the impact of patients' use of eHealth tools on self-reporting adverse effects and symptoms that promote changes to medication use. Related secondary outcomes were also evaluated. Methods: MEDLINE, EMBASE, and CINAHL were searched from January 1, 2000, to April 25, 2018. Reference lists of relevant systematic reviews and included articles from the literature search were also screened to identify relevant studies. Title, abstract, and full-text review as well as data extraction and risk of bias assessment were performed independently by 2 reviewers. Due to high heterogeneity, results were not meta-analyzed and instead presented as a narrative synthesis. Results: A total of 14 studies, including 13 randomized controlled trials (RCTs) and 1 open-label intervention, were included, from which 11 unique eHealth tools were identified. In addition, 14 RCTs found statistically significant increases in positive medication changes as a result of using eHealth tools, as did the single open-label study. Moreover, 8 RCTs found improvement in patient symptoms following eHealth tool use, especially in adolescent asthma patients. Furthermore, 3 RCTs showed that eHealth tools might improve patient self-efficacy and self-management of chronic disease. Little or no evidence was found to support the effectiveness of eHealth tools at improving medication recommendations and reconciliation by clinicians, medication-use behavior, health service utilization, adverse effects, quality of life, or patient satisfaction. eHealth tools with multifaceted functionalities and those allowing direct patient-provider communication may be more effective at improving patient self-management and self-efficacy. Conclusions: Evidence suggests that the use of eHealth tools may improve patient symptoms and lead to medication changes. Patients generally found eHealth tools useful in improving communication with health care providers. Moreover, health-related outcomes among frequent eHealth tool users improved in comparison with individuals who did not use eHealth tools frequently. Implementation issues such as poor patient engagement and poor clinician workflow integration were identified. More high-quality research is needed to explore how eHealth tools can be used to effectively manage use of medications to improve medication management and patient outcomes. ", doi="10.2196/jmir.9284", url="https://www.jmir.org/2018/12/e294/", url="http://www.ncbi.nlm.nih.gov/pubmed/30563822" } @Article{info:doi/10.2196/12159, author="Li, Fei and Liu, Weisong and Yu, Hong", title="Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning", journal="JMIR Med Inform", year="2018", month="Nov", day="26", volume="6", number="4", pages="e12159", keywords="adverse drug event", keywords="deep learning", keywords="multi-task learning", keywords="named entity recognition", keywords="natural language processing", keywords="relation extraction", abstract="Background: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs. Objective: We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps---named entity recognition and relation extraction---our second objective was to improve the deep learning model using multi-task learning between the two steps. Methods: We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively. Results: Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9\%), which is significantly higher than that (F1=61.7\%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8\%, boosting the F1 to 66.7\%, whereas RegMTL and LearnMTL failed to boost the performance. Conclusions: Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning. ", doi="10.2196/12159", url="http://medinform.jmir.org/2018/4/e12159/", url="http://www.ncbi.nlm.nih.gov/pubmed/30478023" } @Article{info:doi/10.2196/10466, author="K{\"u}rzinger, Marie-Laure and Sch{\"u}ck, St{\'e}phane and Texier, Nathalie and Abdellaoui, Redhouane and Faviez, Carole and Pouget, Julie and Zhang, Ling and Tcherny-Lessenot, St{\'e}phanie and Lin, Stephen and Juhaeri, Juhaeri", title="Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis", journal="J Med Internet Res", year="2018", month="Nov", day="20", volume="20", number="11", pages="e10466", keywords="adverse event", keywords="internet", keywords="medical forums", keywords="pharmacovigilance", keywords="signal detection", keywords="signals of disproportionate reporting", keywords="social media", abstract="Background: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). Objective: This study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. Methods: Messages posted on patients' forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. Results: The comparison analysis showed that the sensitivity ranged from 29\% to 50.6\%, the specificity from 86.1\% to 95.5\%, the PPV from 51.2\% to 75.4\%, the NPV from 68.5\% to 91.6\%, and the accuracy from 68\% to 87.7\%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38\% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. Conclusions: The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38\% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals. ", doi="10.2196/10466", url="http://www.jmir.org/2018/11/e10466/", url="http://www.ncbi.nlm.nih.gov/pubmed/30459145" } @Article{info:doi/10.2196/11085, author="Park, Hyun So and Hong, Hee Song", title="Identification of Primary Medication Concerns Regarding Thyroid Hormone Replacement Therapy From Online Patient Medication Reviews: Text Mining of Social Network Data", journal="J Med Internet Res", year="2018", month="Oct", day="24", volume="20", number="10", pages="e11085", keywords="medication counseling", keywords="social network data", keywords="primary medication concerns", keywords="satisfaction with levothyroxine treatment", abstract="Background: Patients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levothyroxine can help improve the treatment outcomes of THRT. Objective: This study aimed to (1) identify the distinctive themes in patient concerns regarding THRT, (2) determine whether patients have unique primary medication concerns specific to their demographics, and (3) determine the predictability of primary medication concerns on patient treatment satisfaction. Methods: We collected patient reviews from WebMD in the United States (1037 reviews about generic levothyroxine and 1075 reviews about the brand version) posted between September 1, 2007, and January 30, 2017. We used natural language processing to identify the themes of medication concerns. Multiple regression analyses were conducted in order to examine the predictability of the primary medication concerns on patient treatment satisfaction. Results: Natural language processing of the patient reviews of levothyroxine posted on a social networking site produced 6 distinctive themes of patient medication concerns related to levothyroxine treatment: how to take the drug, treatment initiation, dose adjustment, symptoms of pain, generic substitutability, and appearance. Patients had different primary medication concerns unique to their gender, age, and treatment duration. Furthermore, treatment satisfaction on levothyroxine depended on what primary medication concerns the patient had. Conclusions: Natural language processing of text content available on social media could identify different themes of patient medication concerns that can be validated in future studies to inform the design of tailored medication counseling for improved patient treatment satisfaction. ", doi="10.2196/11085", url="http://www.jmir.org/2018/10/e11085/", url="http://www.ncbi.nlm.nih.gov/pubmed/30355555" } @Article{info:doi/10.2196/11021, author="Usui, Misa and Aramaki, Eiji and Iwao, Tomohide and Wakamiya, Shoko and Sakamoto, Tohru and Mochizuki, Mayumi", title="Extraction and Standardization of Patient Complaints from Electronic Medication Histories for Pharmacovigilance: Natural Language Processing Analysis in Japanese", journal="JMIR Med Inform", year="2018", month="Sep", day="27", volume="6", number="3", pages="e11021", keywords="adverse drug events", keywords="natural language processing", keywords="medical informatics", keywords="medication history", keywords="pharmacovigilance", abstract="Background: Despite the growing number of studies using natural language processing for pharmacovigilance, there are few reports on manipulating free text patient information in Japanese. Objective: This study aimed to establish a method of extracting and standardizing patient complaints from electronic medication histories accumulated in a Japanese community pharmacy for the detection of possible adverse drug event (ADE) signals. Methods: Subjective information included in electronic medication history data provided by a Japanese pharmacy operating in Hiroshima, Japan from September 1, 2015 to August 31, 2016, was used as patients' complaints. We formulated search rules based on morphological analysis and daily (nonmedical) speech and developed a system that automatically executes the search rules and annotates free text data with International Classification of Diseases, Tenth Revision (ICD-10) codes. The performance of the system was evaluated through comparisons with data manually annotated by health care workers for a data set of 5000 complaints. Results: Of 5000 complaints, the system annotated 2236 complaints with ICD-10 codes, whereas health care workers annotated 2348 statements. There was a match in the annotation of 1480 complaints between the system and manual work. System performance was .66 regarding precision, .63 in recall, and .65 for the F-measure. Conclusions: Our results suggest that the system may be helpful in extracting and standardizing patients' speech related to symptoms from massive amounts of free text data, replacing manual work. After improving the extraction accuracy, we expect to utilize this system to detect signals of possible ADEs from patients' complaints in the future. ", doi="10.2196/11021", url="http://medinform.jmir.org/2018/3/e11021/", url="http://www.ncbi.nlm.nih.gov/pubmed/30262450" } @Article{info:doi/10.2196/10070, author="Schoen, W. Martin and Basch, Ethan and Hudson, L. Lori and Chung, E. Arlene and Mendoza, R. Tito and Mitchell, A. Sandra and St. Germain, Diane and Baumgartner, Paul and Sit, Laura and Rogak, J. Lauren and Shouery, Marwan and Shalley, Eve and Reeve, B. Bryce and Fawzy, R. Maria and Bhavsar, A. Nrupen and Cleeland, Charles and Schrag, Deborah and Dueck, C. Amylou and Abernethy, P. Amy", title="Software for Administering the National Cancer Institute's Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events: Usability Study", journal="JMIR Hum Factors", year="2018", month="Jul", day="16", volume="5", number="3", pages="e10070", keywords="usability", keywords="patient-reported outcomes", keywords="symptoms", keywords="adverse events", keywords="PRO-CTCAE", keywords="cancer clinical trials", abstract="Background: The US National Cancer Institute (NCI) developed software to gather symptomatic adverse events directly from patients participating in clinical trials. The software administers surveys to patients using items from the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) through Web-based or automated telephone interfaces and facilitates the management of survey administration and the resultant data by professionals (clinicians and research associates). Objective: The purpose of this study was to iteratively evaluate and improve the usability of the PRO-CTCAE software. Methods: Heuristic evaluation of the software functionality was followed by semiscripted, think-aloud protocols in two consecutive rounds of usability testing among patients with cancer, clinicians, and research associates at 3 cancer centers. We conducted testing with patients both in clinics and at home (remotely) for both Web-based and telephone interfaces. Furthermore, we refined the software between rounds and retested. Results: Heuristic evaluation identified deviations from the best practices across 10 standardized categories, which informed initial software improvement. Subsequently, we conducted user-based testing among 169 patients and 47 professionals. Software modifications between rounds addressed identified issues, including difficulty using radio buttons, absence of survey progress indicators, and login problems (for patients) as well as scheduling of patient surveys (for professionals). The initial System Usability Scale (SUS) score for the patient Web-based interface was 86 and 82 (P=.22) before and after modifications, respectively, whereas the task completion score was 4.47, which improved to 4.58 (P=.39) after modifications. Following modifications for professional users, the SUS scores improved from 71 to 75 (P=.47), and the mean task performance improved significantly (4.40 vs 4.02; P=.001). Conclusions: Software modifications, informed by rigorous assessment, rendered a usable system, which is currently used in multiple NCI-sponsored multicenter cancer clinical trials. Trial Registration: ClinicalTrials.gov NCT01031641; https://clinicaltrials.gov/ct2/show/NCT01031641 (Archived by WebCite at http://www.webcitation.org/708hTjlTl) ", doi="10.2196/10070", url="http://humanfactors.jmir.org/2018/3/e10070/", url="http://www.ncbi.nlm.nih.gov/pubmed/30012546" } @Article{info:doi/10.2196/10248, author="Peddie, David and Small, S. Serena and Badke, Katherin and Bailey, Chantelle and Balka, Ellen and Hohl, M. Corinne", title="Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset", journal="JMIR Med Inform", year="2018", month="Jun", day="28", volume="6", number="2", pages="e10248", keywords="adverse drug event", keywords="adverse drug reaction", keywords="data fields", keywords="dataset", keywords="reporting", keywords="pharmacovigilance", keywords="mixed-methods", keywords="clinician-informed design", abstract="Background: Patients commonly transition between health care settings, requiring care providers to transfer medication utilization information. Yet, information sharing about adverse drug events (ADEs) remains nonstandardized. Objective: The objective of our study was to describe a minimum required dataset for clinicians to document and communicate ADEs to support clinical decision making and improve patient safety. Methods: We used mixed-methods analysis to design a minimum required dataset for ADE documentation and communication. First, we completed a systematic review of the existing ADE reporting systems. After synthesizing reporting concepts and data fields, we conducted fieldwork to inform the design of a preliminary reporting form. We presented this information to clinician end-user groups to establish a recommended dataset. Finally, we pilot-tested and refined the dataset in a paper-based format. Results: We evaluated a total of 1782 unique data fields identified in our systematic review that describe the reporter, patient, ADE, and suspect and concomitant drugs. Of these, clinicians requested that 26 data fields be integrated into the dataset. Avoiding the need to report information already available electronically, reliance on prospective rather than retrospective causality assessments, and omitting fields deemed irrelevant to clinical care were key considerations. Conclusions: By attending to the information needs of clinicians, we developed a standardized dataset for adverse drug event reporting. This dataset can be used to support communication between care providers and integrated into electronic systems to improve patient safety. If anonymized, these standardized data may be used for enhanced pharmacovigilance and research activities. ", doi="10.2196/10248", url="http://medinform.jmir.org/2018/2/e10248/", url="http://www.ncbi.nlm.nih.gov/pubmed/29954724" } @Article{info:doi/10.2196/jmir.9870, author="Keller, Sophie Michelle and Mosadeghi, Sasan and Cohen, R. Erica and Kwan, James and Spiegel, Ross Brennan Mason", title="Reproductive Health and Medication Concerns for Patients With Inflammatory Bowel Disease: Thematic and Quantitative Analysis Using Social Listening", journal="J Med Internet Res", year="2018", month="Jun", day="11", volume="20", number="6", pages="e206", keywords="pregnancy", keywords="breastfeeding", keywords="reproductive health", keywords="social media", keywords="medication adherence", keywords="infodemiology", keywords="pharmacovigilance", abstract="Background: Inflammatory bowel disease (IBD) affects many individuals of reproductive age. Most IBD medications are safe to use during pregnancy and breastfeeding; however, observational studies find that women with IBD have higher rates of voluntary childlessness due to fears about medication use during pregnancy. Understanding why and how individuals with IBD make decisions about medication adherence during important reproductive periods can help clinicians address patient fears about medication use. Objective: The objective of this study was to gain a more thorough understanding of how individuals taking IBD medications during key reproductive periods make decisions about their medication use. Methods: We collected posts from 3000 social media sites posted over a 3-year period and analyzed the posts using qualitative descriptive content analysis. The first level of analysis, open coding, identified individual concepts present in the social media posts. We subsequently created a codebook from significant or frequently occurring codes in the data. After creating the codebook, we reviewed the data and coded using our focused codes. We organized the focused codes into larger thematic categories. Results: We identified 7 main themes in 1818 social media posts. Individuals used social media to (1) seek advice about medication use related to reproductive health (13.92\%, 252/1818); (2) express beliefs about the safety of IBD therapies (7.43\%, 135/1818); (3) discuss personal experiences with medication use (16.72\%, 304/1818); (4) articulate fears and anxieties about the safety of IBD therapies (11.55\%, 210/1818); (5) discuss physician-patient relationships (3.14\%, 57/1818); (6) address concerns around conception, infertility, and IBD medications (17.38\%, 316/1818); and (7) talk about IBD symptoms during and after pregnancy and breastfeeding periods (11.33\%, 206/1818). Conclusions: Beliefs around medication safety play an important role in whether individuals with IBD decide to take medications during pregnancy and breastfeeding. Having a better understanding about why patients stop or refuse to take certain medications during key reproductive periods may allow clinicians to address specific beliefs and attitudes during office visits. ", doi="10.2196/jmir.9870", url="http://www.jmir.org/2018/6/e206/", url="http://www.ncbi.nlm.nih.gov/pubmed/29891471" } @Article{info:doi/10.2196/cancer.7952, author="Kalf, RJ Rachel and Makady, Amr and ten Ham, MT Renske and Meijboom, Kim and Goettsch, G. Wim and ", title="Use of Social Media in the Assessment of Relative Effectiveness: Explorative Review With Examples From Oncology", journal="JMIR Cancer", year="2018", month="Jun", day="08", volume="4", number="1", pages="e11", keywords="social media", keywords="relative effectiveness", keywords="real-world data", keywords="patient reported outcomes", abstract="Background: An element of health technology assessment constitutes assessing the clinical effectiveness of drugs, generally called relative effectiveness assessment. Little real-world evidence is available directly after market access, therefore randomized controlled trials are used to obtain information for relative effectiveness assessment. However, there is growing interest in using real-world data for relative effectiveness assessment. Social media may provide a source of real-world data. Objective: We assessed the extent to which social media-generated health data has provided insights for relative effectiveness assessment. Methods: An explorative literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify examples in oncology where health data were collected using social media. Scientific and grey literature published between January 2010 and June 2016 was identified by four reviewers, who independently screened studies for eligibility and extracted data. A descriptive qualitative analysis was performed. Results: Of 1032 articles identified, eight were included: four articles identified adverse events in response to cancer treatment, three articles disseminated quality of life surveys, and one study assessed the occurrence of disease-specific symptoms. Several strengths of social media-generated health data were highlighted in the articles, such as efficient collection of patient experiences and recruiting patients with rare diseases. Conversely, limitations included validation of authenticity and presence of information and selection bias. Conclusions: Social media may provide a potential source of real-world data for relative effectiveness assessment, particularly on aspects such as adverse events, symptom occurrence, quality of life, and adherence behavior. This potential has not yet been fully realized and the degree of usefulness for relative effectiveness assessment should be further explored. ", doi="10.2196/cancer.7952", url="http://cancer.jmir.org/2018/1/e11/", url="http://www.ncbi.nlm.nih.gov/pubmed/29884607" } @Article{info:doi/10.2196/jmir.9901, author="Musy, N. Sarah and Ausserhofer, Dietmar and Schwendimann, Ren{\'e} and Rothen, Ulrich Hans and Jeitziner, Marie-Madlen and Rutjes, WS Anne and Simon, Michael", title="Trigger Tool--Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review", journal="J Med Internet Res", year="2018", month="May", day="30", volume="20", number="5", pages="e198", keywords="patient safety", keywords="electronic health records", keywords="patient harm", keywords="review, systematic", abstract="Background: Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. Objective: The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. Methods: PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. Results: A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0\% to 17.9\%, with a median of 0.8\%. The positive predictive value of all triggers to detect adverse events ranged from 0\% to 100\% across studies, with a median of 40\%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8\% to 60\%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20\% to 91\%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9\% to 83.3\%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0\% to 60\%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4\%, 10.5\%, 71.1\%, and 68.4\% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. Conclusions: We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies. ", doi="10.2196/jmir.9901", url="http://www.jmir.org/2018/5/e198/" } @Article{info:doi/10.2196/publichealth.8214, author="Bollegala, Danushka and Maskell, Simon and Sloane, Richard and Hajne, Joanna and Pirmohamed, Munir", title="Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach", journal="JMIR Public Health Surveill", year="2018", month="May", day="09", volume="4", number="2", pages="e51", keywords="machine learning", keywords="ADR detection", keywords="causality", keywords="lexical patterns", keywords="causality detection", keywords="support vector machines", abstract="Background: Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. Objective: This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. Methods: To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Results: Our proposed method obtains an ADR detection accuracy of 74\% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. Conclusions: By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction--related events. ", doi="10.2196/publichealth.8214", url="http://publichealth.jmir.org/2018/2/e51/", url="http://www.ncbi.nlm.nih.gov/pubmed/29743155" } @Article{info:doi/10.2196/publichealth.9361, author="Munkhdalai, Tsendsuren and Liu, Feifan and Yu, Hong", title="Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning", journal="JMIR Public Health Surveill", year="2018", month="Apr", day="25", volume="4", number="2", pages="e29", keywords="medical informatics applications", keywords="drug-related side effects and adverse reactions", keywords="neural networks", keywords="natural language processing", keywords="electronic health records", abstract="Background: Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data. Objective: To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations. Methods: We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types. Results: Our results show that the SVM model achieved the best average F1-score of 89.1\% on test data, outperforming the long short-term memory (LSTM) model with attention (F1-score of 65.72\%) as well as the rule induction baseline system (F1-score of 7.47\%) by a large margin. The bidirectional LSTM model with attention achieved the best performance among different RNN models. With the inclusion of additional features in the LSTM model, its performance can be boosted to an average F1-score of 77.35\%. Conclusions: It shows that classical learning models (SVM) remains advantageous over deep learning models (RNN variants) for clinical relation identification, especially for long-distance intersentential relations. However, RNNs demonstrate a great potential of significant improvement if more training data become available. Our work is an important step toward mining EHRs to improve the efficacy of drug safety surveillance. Most importantly, the annotated data used in this study will be made publicly available, which will further promote drug safety research in the community. ", doi="10.2196/publichealth.9361", url="http://publichealth.jmir.org/2018/2/e29/", url="http://www.ncbi.nlm.nih.gov/pubmed/29695376" } @Article{info:doi/10.2196/humanfactors.8319, author="Dijkstra, Elske Nienke and Sino, Maria Carolina Geertruida and Heerdink, Rob Eibert and Schuurmans, Joanna Marieke", title="Development of eHOME, a Mobile Instrument for Reporting, Monitoring, and Consulting Drug-Related Problems in Home Care: Human-Centered Design Study", journal="JMIR Hum Factors", year="2018", month="Mar", day="07", volume="5", number="1", pages="e10", keywords="primary care", keywords="home care", keywords="eHealth", keywords="mHealth", abstract="Background: Home care patients often use many medications and are prone to drug-related problems (DRPs). For the management of problems related to drug use, home care could add to the multidisciplinary expertise of general practitioners (GPs) and pharmacists. The home care observation of medication-related problems by home care employees (HOME)-instrument is paper-based and assists home care workers in reporting potential DRPs. To facilitate the multiprofessional consultation, a digital report of DRPs from the HOME-instrument and digital monitoring and consulting of DRPs between home care and general practices and pharmacies is desired. Objective: The objective of this study was to develop an electronic HOME system (eHOME), a mobile version of the HOME-instrument that includes a monitoring and a consulting system for primary care. Methods: The development phase of the Medical Research Council (MRC) framework was followed in which an iterative human-centered design (HCD) approach was applied. The approach involved a Delphi round for the context of use and user requirements analysis of the digital HOME-instrument and the monitoring and consulting system followed by 2 series of pilots for testing the usability and redesign. Results: By using an iterative design approach and by involving home care workers, GPs, and pharmacists throughout the process as informants, design partners, and testers, important aspects that were crucial for system realization and user acceptance were revealed. Through the report webpage interface, which includes the adjusted content of the HOME-instrument and added home care practice--based problems, home care workers can digitally report observed DRPs. Furthermore, it was found that the monitoring and consulting webpage interfaces enable digital consultation between home care and general practices and pharmacies. The webpages were considered convenient, clear, easy, and usable. Conclusions: By employing an HCD approach, the eHOME-instrument was found to be an easy-to-use system. The systematic approach promises a valuable contribution for the future development of digital mobile systems of paper-based tools. ", doi="10.2196/humanfactors.8319", url="http://humanfactors.jmir.org/2018/1/e10/", url="http://www.ncbi.nlm.nih.gov/pubmed/29514771" } @Article{info:doi/10.2196/resprot.9078, author="Fleming, N. James and Treiber, Frank and McGillicuddy, John and Gebregziabher, Mulugeta and Taber, J. David", title="Improving Transplant Medication Safety Through a Pharmacist-Empowered, Patient-Centered, mHealth-Based Intervention: TRANSAFE Rx Study Protocol", journal="JMIR Res Protoc", year="2018", month="Mar", day="02", volume="7", number="3", pages="e59", keywords="telemedicine", keywords="mhealth", keywords="transplant", keywords="clinical trial", keywords="errors", keywords="adherence", abstract="Background: Medication errors, adverse drug events, and nonadherence are the predominant causes of graft loss in kidney transplant recipients and lead to increased healthcare utilization. Research has demonstrated that clinical pharmacists have the unique education and training to identify these events early and develop strategies to mitigate or prevent downstream sequelae. In addition, studies utilizing mHealth interventions have demonstrated success in improving the control of chronic conditions that lead to kidney transplant deterioration. Objective: The goal of the prospective, randomized TRANSAFE Rx study is to measure the clinical and economic effectiveness of a pharmacist-led, mHealth-based intervention, as compared to usual care, in kidney transplant recipients. Methods: TRANSAFE Rx is a 12-month, parallel, two-arm, 1:1 randomized controlled clinical trial involving 136 participants (68 in each arm) and measuring the clinical and economic effectiveness of a pharmacist-led intervention which utilizes an innovative mobile health application to improve medication safety and health outcomes, as compared to usual posttransplant care. Results: The primary outcome measure of this study will be the incidence and severity of MEs and ADRs, which will be identified, categorized, and compared between the intervention and control cohorts. The exploratory outcome measures of this study are to compare the incidence and severity of acute rejections, infections, graft function, graft loss, and death between research cohorts and measure the association between medication safety issues and these events. Additional data that will be gathered includes sociodemographics, health literacy, depression, and support. Conclusions: With this report we describe the study design, methods, and outcome measures that will be utilized in the ongoing TRANSAFE Rx clinical trial. Trial Registration: ClinicalTrials.gov NCT03247322: https://clinicaltrials.gov/ct2/show/NCT03247322 (Archived by WebCite at http://www.webcitation.org/6xcSUnuzW) ", doi="10.2196/resprot.9078", url="https://www.researchprotocols.org/2018/3/e59/", url="http://www.ncbi.nlm.nih.gov/pubmed/29500161" } @Article{info:doi/10.2196/publichealth.9282, author="Hohl, M. Corinne and Small, S. Serena and Peddie, David and Badke, Katherin and Bailey, Chantelle and Balka, Ellen", title="Why Clinicians Don't Report Adverse Drug Events: Qualitative Study", journal="JMIR Public Health Surveill", year="2018", month="Feb", day="27", volume="4", number="1", pages="e21", keywords="adverse events", keywords="pharmacovigilance", keywords="drug safety", keywords="adverse drug reaction", keywords="adverse drug event", keywords="electronic health records", keywords="information and technology", keywords="medication reconciliation", keywords="qualitative research", abstract="Background: Adverse drug events are unintended and harmful events related to medications. Adverse drug events are important for patient care, quality improvement, drug safety research, and postmarketing surveillance, but they are vastly underreported. Objective: Our objectives were to identify barriers to adverse drug event documentation and factors contributing to underreporting. Methods: This qualitative study was conducted in 1 ambulatory center, and the emergency departments and inpatient wards of 3 acute care hospitals in British Columbia between March 2014 and December 2016. We completed workplace observations and focus groups with general practitioners, hospitalists, emergency physicians, and hospital and community pharmacists. We analyzed field notes by coding and iteratively analyzing our data to identify emerging concepts, generate thematic and event summaries, and create workflow diagrams. Clinicians validated emerging concepts by applying them to cases from their clinical practice. Results: We completed 238 hours of observations during which clinicians investigated 65 suspect adverse drug events. The observed events were often complex and diagnosed over time, requiring the input of multiple providers. Providers documented adverse drug events in charts to support continuity of care but never reported them to external agencies. Providers faced time constraints, and reporting would have required duplication of documentation. Conclusions: Existing reporting systems are not suited to capture the complex nature of adverse drug events or adapted to workflow and are simply not used by frontline clinicians. Systems that are integrated into electronic medical records, make use of existing data to avoid duplication of documentation, and generate alerts to improve safety may address the shortcomings of existing systems and generate robust adverse drug event data as a by-product of safer care. ", doi="10.2196/publichealth.9282", url="http://publichealth.jmir.org/2018/1/e21/", url="http://www.ncbi.nlm.nih.gov/pubmed/29487041" } @Article{info:doi/10.2196/publichealth.7726, author="Simpson, S. Sean and Adams, Nikki and Brugman, M. Claudia and Conners, J. Thomas", title="Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study", journal="JMIR Public Health Surveill", year="2018", month="Jan", day="08", volume="4", number="1", pages="e2", keywords="natural language processing", keywords="street drugs", keywords="social media", keywords="vocabulary", abstract="Background: With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time. This study describes the initial results from an innovative collaboration between substance use epidemiologists and linguistic scientists employing techniques from the field of natural language processing to examine drug-related terms in a sample of tweets from the United States. Objective: The objective of this study was to assess the feasibility of using distributed word-vector embeddings trained on social media data to uncover previously unknown (to researchers) drug terms. Methods: In this pilot study, we trained a continuous bag of words (CBOW) model of distributed word-vector embeddings on a Twitter dataset collected during July 2016 (roughly 884.2 million tokens). We queried the trained word embeddings for terms with high cosine similarity (a proxy for semantic relatedness) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance. This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology. Results: The method described here produced a list of 200 candidate terms for the target substance (marijuana). Of these 200 candidates, 115 were determined to in fact relate to marijuana (65 terms for the substance itself, 50 terms related to paraphernalia). This included 30 terms which were used to refer to the target substance in the corpus yet did not appear on the expert-generated list and were therefore considered to be successful cases of uncovering novel drug terminology. Several of these novel terms appear to have been introduced as recently as 1 or 2 months before the corpus time slice used to train the word embeddings. Conclusions: Though the precision of the method described here is low enough as to still necessitate human review of any candidate term lists generated in such a manner, the fact that this process was able to detect 30 novel terms for the target substance based only on one month's worth of Twitter data is highly promising. We see this pilot study as an important proof of concept and a first step toward producing a fully automated drug term discovery system capable of tracking emerging NPS terms in real time. ", doi="10.2196/publichealth.7726", url="http://publichealth.jmir.org/2018/1/e2/", url="http://www.ncbi.nlm.nih.gov/pubmed/29311050" } @Article{info:doi/10.2196/resprot.6463, author="Bousquet, Cedric and Dahamna, Badisse and Guillemin-Lanne, Sylvie and Darmoni, J. Stefan and Faviez, Carole and Huot, Charles and Katsahian, Sandrine and Leroux, Vincent and Pereira, Suzanne and Richard, Christophe and Sch{\"u}ck, St{\'e}phane and Souvignet, Julien and Lillo-Le Lou{\"e}t, Agn{\`e}s and Texier, Nathalie", title="The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process", journal="JMIR Res Protoc", year="2017", month="Sep", day="21", volume="6", number="9", pages="e179", keywords="pharmacovigilance", keywords="social media", keywords="big data", keywords="natural language processing", keywords="medical terminology", abstract="Background: Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective: This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods: In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results: Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions: We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. ", doi="10.2196/resprot.6463", url="http://www.researchprotocols.org/2017/9/e179/", url="http://www.ncbi.nlm.nih.gov/pubmed/28935617" } @Article{info:doi/10.2196/publichealth.6577, author="Abdellaoui, Redhouane and Sch{\"u}ck, St{\'e}phane and Texier, Nathalie and Burgun, Anita", title="Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help?", journal="JMIR Public Health Surveill", year="2017", month="Jun", day="22", volume="3", number="2", pages="e36", keywords="pharmacovigilance", keywords="social media", keywords="text mining", keywords="Gaussian mixture model", keywords="EM algorithm", keywords="clustering", keywords="density estimation", abstract="Background: With the increasing popularity of Web 2.0 applications, social media has made it possible for individuals to post messages on adverse drug reactions. In such online conversations, patients discuss their symptoms, medical history, and diseases. These disorders may correspond to adverse drug reactions (ADRs) or any other medical condition. Therefore, methods must be developed to distinguish between false positives and true ADR declarations. Objective: The aim of this study was to investigate a method for filtering out disorder terms that did not correspond to adverse events by using the distance (as number of words) between the drug term and the disorder or symptom term in the post. We hypothesized that the shorter the distance between the disorder name and the drug, the higher the probability to be an ADR. Methods: We analyzed a corpus of 648 messages corresponding to a total of 1654 (drug and disorder) pairs from 5 French forums using Gaussian mixture models and an expectation-maximization (EM) algorithm . Results: The distribution of the distances between the drug term and the disorder term enabled the filtering of 50.03\% (733/1465) of the disorders that were not ADRs. Our filtering strategy achieved a precision of 95.8\% and a recall of 50.0\%. Conclusions: This study suggests that such distance between terms can be used for identifying false positives, thereby improving ADR detection in social media. ", doi="10.2196/publichealth.6577", url="http://publichealth.jmir.org/2017/2/e36/", url="http://www.ncbi.nlm.nih.gov/pubmed/28642212" } @Article{info:doi/10.2196/publichealth.6396, author="Alvaro, Nestor and Miyao, Yusuke and Collier, Nigel", title="TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations", journal="JMIR Public Health Surveill", year="2017", month="May", day="03", volume="3", number="2", pages="e24", keywords="Twitter", keywords="PubMed", keywords="corpus", keywords="pharmacovigilance", keywords="natural language processing", keywords="text mining", keywords="annotation", abstract="Background: Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. Objective: This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. Methods: We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. Results: The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. Conclusions: We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. ", doi="10.2196/publichealth.6396", url="http://publichealth.jmir.org/2017/2/e24/", url="http://www.ncbi.nlm.nih.gov/pubmed/28468748" } @Article{info:doi/10.2196/publichealth.6174, author="Anderson, S. Laurie and Bell, G. Heidi and Gilbert, Michael and Davidson, E. Julie and Winter, Christina and Barratt, J. Monica and Win, Beta and Painter, L. Jeffery and Menone, Christopher and Sayegh, Jonathan and Dasgupta, Nabarun", title="Using Social Listening Data to Monitor Misuse and Nonmedical Use of Bupropion: A Content Analysis", journal="JMIR Public Health Surveill", year="2017", month="Feb", day="1", volume="3", number="1", pages="e6", keywords="social media", keywords="Internet", keywords="prescription drug misuse", keywords="substance-related disorders", keywords="pharmacovigilance", keywords="harm reduction", keywords="community-based participatory research", keywords="bupropion", keywords="amitriptyline", keywords="venlafaxine hydrochloride", abstract="Background: The nonmedical use of pharmaceutical products has become a significant public health concern. Traditionally, the evaluation of nonmedical use has focused on controlled substances with addiction risk. Currently, there is no effective means of evaluating the nonmedical use of noncontrolled antidepressants. Objective: Social listening, in the context of public health sometimes called infodemiology or infoveillance, is the process of identifying and assessing what is being said about a company, product, brand, or individual, within forms of electronic interactive media. The objectives of this study were (1) to determine whether content analysis of social listening data could be utilized to identify posts discussing potential misuse or nonmedical use of bupropion and two comparators, amitriptyline and venlafaxine, and (2) to describe and characterize these posts. Methods: Social listening was performed on all publicly available posts cumulative through July 29, 2015, from two harm-reduction Web forums, Bluelight and Opiophile, which mentioned the study drugs. The acquired data were stripped of personally identifiable identification (PII). A set of generic, brand, and vernacular product names was used to identify product references in posts. Posts were obtained using natural language processing tools to identify vernacular references to drug misuse-related Preferred Terms from the English Medical Dictionary for Regulatory Activities (MedDRA) version 18 terminology. Posts were reviewed manually by coders, who extracted relevant details. Results: A total of 7756 references to at least one of the study antidepressants were identified within posts gathered for this study. Of these posts, 668 (8.61\%, 668/7756) referenced misuse or nonmedical use of the drug, with bupropion accounting for 438 (65.6\%, 438/668). Of the 668 posts, nonmedical use was discouraged by 40.6\% (178/438), 22\% (22/100), and 18.5\% (24/130) and encouraged by 12.3\% (54/438), 10\% (10/100), and 10.8\% (14/130) for bupropion, amitriptyline, and venlafaxine, respectively. The most commonly reported desired effects were similar to stimulants with bupropion, sedatives with amitriptyline, and dissociatives with venlafaxine. The nasal route of administration was most frequently reported for bupropion, whereas the oral route was most frequently reported for amitriptyline and venlafaxine. Bupropion and venlafaxine were most commonly procured from health care providers, whereas amitriptyline was most commonly obtained or stolen from a third party. The Fleiss kappa for interrater agreement among 20 items with 7 categorical response options evaluated by all 11 raters was 0.448 (95\% CI 0.421-0.457). Conclusions: Social listening, conducted in collaboration with harm-reduction Web forums, offers a valuable new data source that can be used for monitoring nonmedical use of antidepressants. Additional work on the capabilities of social listening will help further delineate the benefits and limitations of this rapidly evolving data source. ", doi="10.2196/publichealth.6174", url="http://publichealth.jmir.org/2017/1/e6/", url="http://www.ncbi.nlm.nih.gov/pubmed/28148472" } @Article{info:doi/10.2196/publichealth.6327, author="Daniulaityte, Raminta and Chen, Lu and Lamy, R. Francois and Carlson, G. Robert and Thirunarayan, Krishnaprasad and Sheth, Amit", title="``When `Bad' is `Good''': Identifying Personal Communication and Sentiment in Drug-Related Tweets", journal="JMIR Public Health Surveill", year="2016", month="Oct", day="24", volume="2", number="2", pages="e162", keywords="social media", keywords="Twitter", keywords="cannabis", keywords="synthetic cannabinoids", keywords="machine learning", keywords="sentiment analysis", keywords="eDrugTrends", abstract="Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid--related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25\% (1000/4000) were used to build source classifiers and 75\% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88\%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-scores. One-tailed t test was used to determine if differences in F-scores were statistically significant. Results: In multiclass source classification, the use of expanded URLs did not contribute to significant improvement in classifier performance (0.7972 vs 0.8102 for SVM, P=.19). In binary classification, the identification of all source categories improved significantly when unshortened URLs were used, with personal communication tweets benefiting the most (0.8736 vs 0.8200, P<.001). In multiclass sentiment classification Approach 1, SVM (0.6723) performed similarly to NB (0.6683) and LR (0.6703). In Approach 2, SVM (0.7062) did not differ from NB (0.6980, P=.13) or LR (F=0.6931, P=.05), but it was over 40\% more accurate than VADER (F=0.5030, P<.001). In multiclass task, improvements in sentiment classification (Approach 2 vs Approach 1) did not reach statistical significance (eg, SVM: 0.7062 vs 0.6723, P=.052). In binary sentiment classification (positive vs negative), Approach 2 (focus on personal communication tweets only) improved classification results, compared with Approach 1, for LR (0.8752 vs 0.8516, P=.04) and SVM (0.8800 vs 0.8557, P=.045). Conclusions: The study provides an example of the use of supervised machine learning methods to categorize cannabis- and synthetic cannabinoid--related tweets with fairly high accuracy. Use of these content analysis tools along with geographic identification capabilities developed by the eDrugTrends platform will provide powerful methods for tracking regional changes in user opinions related to cannabis and synthetic cannabinoids use over time and across different regions. ", doi="10.2196/publichealth.6327", url="http://publichealth.jmir.org/2016/2/e162/", url="http://www.ncbi.nlm.nih.gov/pubmed/27777215" } @Article{info:doi/10.2196/resprot.5621, author="Rastegar-Mojarad, Majid and Liu, Hongfang and Nambisan, Priya", title="Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study", journal="JMIR Res Protoc", year="2016", month="Jun", day="16", volume="5", number="2", pages="e121", keywords="social media", keywords="drug repurposing", keywords="natural language processing", keywords="patient comments", abstract="Background: Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications. Objective: Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing. Methods: We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects. Results: The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates. Conclusions: To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing. ", doi="10.2196/resprot.5621", url="http://www.researchprotocols.org/2016/2/e121/", url="http://www.ncbi.nlm.nih.gov/pubmed/27311964" } @Article{info:doi/10.2196/publichealth.5366, author="Richardson, Luke Jonathan and Stephens, Sally and Thomas, Lynton Simon Hugh and Jamry-Dziurla, Anna and de Jong-van den Berg, Lolkje and Zetstra - van der Woude, Priscilla and Laursen, Maja and Hliva, Valerie and Mt-Isa, Shahrul and Bourke, Alison and Dreyer, A. Nancy and Blackburn, CF Stella", title="An International Study of the Ability and Cost-Effectiveness of Advertising Methods to Facilitate Study Participant Self-Enrolment Into a Pilot Pharmacovigilance Study During Early Pregnancy", journal="JMIR Public Health Surveill", year="2016", month="Mar", day="18", volume="2", number="1", pages="e13", keywords="teratogen", keywords="surveillance", keywords="pregnancy", keywords="pharmacovigilance recruitment", keywords="advertisement", abstract="Background: Knowledge of the fetal effects of maternal medication use in pregnancy is often inadequate and current pregnancy pharmacovigilance (PV) surveillance methods have important limitations. Patient self-reporting may be able to mitigate some of these limitations, providing an adequately sized study sample can be recruited. Objective: To compare the ability and cost-effectiveness of several direct-to-participant advertising methods for the recruitment of pregnant participants into a study of self-reported gestational exposures and pregnancy outcomes. Methods: The Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium (PROTECT) pregnancy study is a non-interventional, prospective pilot study of self-reported medication use and obstetric outcomes provided by a cohort of pregnant women that was conducted in Denmark, the Netherlands, Poland, and the United Kingdom. Direct-to-participant advertisements were provided via websites, emails, leaflets, television, and social media platforms. Results: Over a 70-week recruitment period direct-to-participant advertisements engaged 43,234 individuals with the study website or telephone system; 4.78\% (2065/43,234) of which were successfully enrolled and provided study data. Of these 90.4\% (1867/2065) were recruited via paid advertising methods, 23.0\% (475/2065) of whom were in the first trimester of pregnancy. The overall costs per active recruited participant were lowest for email ({\texteuro}23.24) and website ({\texteuro}24.41) advertisements and highest for leaflet ({\texteuro}83.14) and television ({\texteuro}100.89). Website adverts were substantially superior in their ability to recruit participants during their first trimester of pregnancy (317/668, 47.5\%) in comparison with other advertising methods (P<.001). However, we identified international variations in both the cost-effectiveness of the various advertisement methods used and in their ability to recruit participants in early pregnancy. Conclusions: Recruitment of a pregnant cohort using direct-to-participant advertisement methods is feasible, but the total costs incurred are not insubstantial. Future research is needed to identify advertising strategies capable of recruiting large numbers of demographically representative pregnant women, preferentially in early pregnancy. ", doi="10.2196/publichealth.5366", url="http://publichealth.jmir.org/2016/1/e13/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227148" } @Article{info:doi/10.2196/publichealth.4939, author="Dreyer, A. Nancy and Blackburn, CF Stella and Mt-Isa, Shahrul and Richardson, L. Jonathan and Thomas, Simon and Laursen, Maja and Zetstra-van der Woude, Priscilla and Jamry-Dziurla, Anna and Hliva, Valerie and Bourke, Alison and de Jong-van den Berg, Lolkje", title="Direct-to-Patient Research: Piloting a New Approach to Understanding Drug Safety During Pregnancy", journal="JMIR Public Health Surveill", year="2015", month="Dec", day="22", volume="1", number="2", pages="e22", keywords="pharmacovigilance", keywords="direct-to-patient", keywords="drug safety", keywords="validation", abstract="Background: Little is known about the effects of human fetal exposure when a new drug is authorized unless it was specifically developed for use in pregnancy. Since many factors may contribute to adverse fetal effects, having comprehensive information about in utero exposures will enhance our ability to make correct determinations about causality. Objective: The objective of the study was to assess the extent to which women, recruited without the intervention of health care professionals (HCPs), will provide information, suitable for research purposes, via the Internet or by phone on some potential risk factors in pregnancy. Methods: To pilot direct-to-patient research for pharmacovigilance, we conducted a prospective, noninterventional study of medication use and lifestyle factors as part of the Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium (PROTECT) Consortium. Consenting women who self-identified as pregnant and residing in the United Kingdom (UK), Denmark (DK), The Netherlands, or Poland were recruited and could then choose to provide data every 2 or 4 weeks via the Internet or a telephonic interactive voice response system (IVRS). Self-reported drug use was compared with pharmacy register data in DK and with electronic health records in the UK. Results: Recruited women were on average older and more highly educated than the general population. Most respondents chose a frequency of every 4 weeks (56.99\%, 1177/2065). Only 29.83\% (464/1555) of women with due dates occurring during the study provided information on pregnancy outcome. For those responding by Internet, over 90.00\% (1915/2065) reported using >1 pregnancy-related medication, 83.34\% (1721/2065) reported using >1 other medicine, and 23.53\% (486/2065) reported only over-the-counter medications, not counting herbals and dietary supplements. Some respondents (7.16\%, 148/2065) reported that they chose not to take a prescribed medication (mostly medicines for pain or inflammation, and for depression) and 1.30\% (27/2065) reported using medicines that had been prescribed to a friend or family member (oxycodone, paracetamol, and medications for acid-related problems). Relatively few respondents reported using fish oil (4.60\%, 95/2065), other dietary supplements (1.88\%, 39/2065), herbal products (7.07\%, 146/2065), or homeopathic products (1.16\%, 24/2065). Most medications for chronic conditions that were listed in the Danish prescription registry were also self-reported (83.3\%, 145/174 agreement), with larger discrepancies for medications indicated for short-term use (54.0\%, 153/283 agreement) and pregnancy-related medications (66.1\%, 78/118). Conclusions: Self-reported information on medication use as well as other potential teratogenic factors can be collected via the Internet, although recruitment costs are not insubstantial and maintaining follow-up is challenging. Direct data collection from consumers adds detail, but clinical input may be needed to fully understand patients' medical histories and capture birth outcomes. ", doi="10.2196/publichealth.4939", url="http://publichealth.jmir.org/2015/2/e22/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227140" } @Article{info:doi/10.2196/jmir.5144, author="Katsuki, Takeo and Mackey, Ken Tim and Cuomo, Raphael", title="Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data", journal="J Med Internet Res", year="2015", month="Dec", day="16", volume="17", number="12", pages="e280", keywords="social media", keywords="surveillance", keywords="prescription drug abuse", keywords="twitter", keywords="eHealth", keywords="illicit Internet pharmacies", keywords="cyberpharmacies", keywords="infodemiology", keywords="infoveillance", abstract="Background: Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens. Objective: In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies. Methods: Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning. Results: A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72\% of the generic and 12.28\% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72\% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription. Conclusions: This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online. ", doi="10.2196/jmir.5144", url="http://www.jmir.org/2015/12/e280/", url="http://www.ncbi.nlm.nih.gov/pubmed/26677966" } @Article{info:doi/10.2196/publichealth.4605, author="Vergeire-Dalmacion, Godofreda and Castillo-Carandang, T. Nina and Juban, R. Noel and Amarillo, Lourdes Maria and Tagle, Pamela Maria and Baja, S. Emmanuel", title="Texting-Based Reporting of Adverse Drug Reactions to Ensure Patient Safety: A Feasibility Study", journal="JMIR Public Health Surveill", year="2015", month="Nov", day="19", volume="1", number="2", pages="e12", keywords="adverse drug reactions", keywords="pharmacovigilance", keywords="postmarketing", keywords="spontaneous reporting", keywords="texting", abstract="Background: Paper-based adverse drug reaction (ADR) reporting has been in practice for more than 6 decades. Health professionals remain the primary source of reports, while the value of patients' reporting is yet unclear. With the increasing popularity of using electronic gadgets in health, it is expected that the electronic transmission of reports will become the norm within a few years. Objective: The aims of this study are to investigate whether short messaging service or texting can provide an alternative or supplemental method for ADR reporting given the increasing role of mobile phones in health care monitoring; to determine the usefulness of texting in addition to paper-based reporting of ADRs by resident physicians; and to describe the barriers to ADR reporting and estimate the cost for setting up and maintaining a texting-computer reporting system. Methods: This was a pre-post cross-sectional study that measured the number of ADRs texted by 51 resident physicians for 12 months from the Department of Obstetrics and Gynecology and the Department of Adult Medicine of a tertiary government hospital in Manila, Philippines, with 1350-bed capacity. Reports were captured by a texting-computer reporting system. Prior to its implementation, key informant interview and focus group discussion were conducted. Baseline information and practice on the existing paper-based reporting system were culled from the records of the hospital's Pharmacy and Therapeutics Committee. A postintervention survey questionnaire was administered at the end of 12 months. Results: Only 3 ADRs were texted by 51 resident physicians in 12 months (reporting rate 3/51 or 6\%). By contrast, 240 ADRs from the paper-based reporting system from 848 resident physicians of the study hospital were collected and tabulated (reporting rate 240/848 or 28.3\%). Texting ADRs was not efficient because of power interruption, competition with the existing paper-based reporting system, and unforeseen expiration of prepaid text loads/credits. The 3 ADRs texted were a report of vivid dreams and nightmares, a report of disturbing dreams and memory lapses, both of which were due to montelukast use, and a report of hepatitis from an isoniazid/rifampicin fixed-dose combination. Nineteen of 51 resident physicians (37\%) registered in the reporting system responded to the postintervention survey. The most common reasons for not reporting ADRs were no adverse reaction identified 11/19 (58\%) and restrictive reporting syntax 4/19 (21\%). All doctors preferred a free form of reporting. The direct cost of the texting-based reporting system was calculated to be US \$5581.40 and the indirect cost was US \$9989.40. The total cost for texting-based ADR reporting system for 12 months was US \$15,570.79. Conclusions: Reporting of ADRs via texting could be lower compared with an existing ADR paper-based system. Problems of Internet connectivity, reporting syntax, and expiration and reliability of text loads/credits should be addressed while implementing a text-based ADR reporting system in a developing country. ", doi="10.2196/publichealth.4605", url="http://publichealth.jmir.org/2015/2/e12/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227130" } @Article{info:doi/10.2196/jmir.4396, author="Adusumalli, Swarnaseetha and Lee, HueyTyng and Hoi, Qiangze and Koo, Si-Lin and Tan, Beehuat Iain and Ng, Crystal Pauline", title="Assessment of Web-Based Consumer Reviews as a Resource for Drug Performance", journal="J Med Internet Res", year="2015", month="Aug", day="28", volume="17", number="8", pages="e211", keywords="consumer drug reviews", keywords="online drug ratings", keywords="WebMD", keywords="online health websites", abstract="Background: Some health websites provide a public forum for consumers to post ratings and reviews on drugs. Drug reviews are easily accessible and comprehensible, unlike clinical trials and published literature. Because the public increasingly uses the Internet as a source of medical information, it is important to know whether such information is reliable. Objective: We aim to examine whether Web-based consumer drug ratings and reviews can be used as a resource to compare drug performance. Methods: We analyzed 103,411 consumer-generated reviews on 615 drugs used to treat 249 disease conditions from the health website WebMD. Statistical analysis identified 427 drug pairs from 24 conditions for which two drugs treating the same condition had significantly and substantially different satisfaction ratings (with at least a half-point difference between Web-based ratings and P<.01). PubMed and Google Scholar were searched for publications that were assessed for concordance with findings online. Results: Scientific literature was found for 77 out of the 427 drug pairs and compared to findings online. Nearly two-thirds (48/77, 62\%) of the online drug trends with at least a half-point difference in online ratings were supported by published literature (P=.02). For a 1-point online rating difference, the concordance rate increased to 68\% (15/22) (P=.07). The discrepancies between scientific literature and findings online were further examined to obtain more insights into the usability of Web-based consumer-generated reviews. We discovered that (1) drugs with FDA black box warnings or used off-label were rated poorly in Web-based reviews, (2) drugs with addictive properties were rated higher than their counterparts in Web-based reviews, and (3) second-line or alternative drugs were rated higher. In addition, Web-based ratings indicated drug delivery problems. If FDA black box warning labels are used to resolve disagreements between publications and online trends, the concordance rate increases to 71\% (55/77) (P<.001) for a half-point rating difference and 82\% (18/22) for a 1-point rating difference (P=.002). Our results suggest that Web-based reviews can be used to inform patients' drug choices, with certain caveats. Conclusions: Web-based reviews can be viewed as an orthogonal source of information for consumers, physicians, and drug manufacturers to assess the performance of a drug. However, one should be cautious to rely solely on consumer reviews as ratings can be strongly influenced by the consumer experience. ", doi="10.2196/jmir.4396", url="http://www.jmir.org/2015/8/e211/", url="http://www.ncbi.nlm.nih.gov/pubmed/26319108" } @Article{info:doi/10.2196/jmir.4427, author="Callahan, Alison and Pernek, Igor and Stiglic, Gregor and Leskovec, Jure and Strasberg, R. Howard and Shah, Haresh Nigam", title="Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance", journal="J Med Internet Res", year="2015", month="Aug", day="20", volume="17", number="8", pages="e204", keywords="Internet log analysis", keywords="data mining", keywords="physicians", keywords="information-seeking behavior", keywords="drug safety surveillance", abstract="Background: Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance. Objective: To analyze health care professionals' information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource. Methods: Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert. Results: Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert. Conclusions: Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate. ", doi="10.2196/jmir.4427", url="http://www.jmir.org/2015/8/e204/", url="http://www.ncbi.nlm.nih.gov/pubmed/26293444" } @Article{info:doi/10.2196/publichealth.4488, author="Adrover, Cosme and Bodnar, Todd and Huang, Zhuojie and Telenti, Amalio and Salath{\'e}, Marcel", title="Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter", journal="JMIR Public Health Surveill", year="2015", month="Jul", day="27", volume="1", number="2", pages="e7", keywords="Twitter", keywords="HIV", keywords="AIDS", keywords="pharmacovigilance", keywords="mTurk", keywords="mechanical Turk", abstract="Background: Social media platforms are increasingly seen as a source of data on a wide range of health issues. Twitter is of particular interest for public health surveillance because of its public nature. However, the very public nature of social media platforms such as Twitter may act as a barrier to public health surveillance, as people may be reluctant to publicly disclose information about their health. This is of particular concern in the context of diseases that are associated with a certain degree of stigma, such as HIV/AIDS. Objective: The objective of the study is to assess whether adverse effects of HIV drug treatment and associated sentiments can be determined using publicly available data from social media. Methods: We describe a combined approach of machine learning and crowdsourced human assessment to identify adverse effects of HIV drug treatment solely on individual reports posted publicly on Twitter. Starting from a large dataset of 40 million tweets collected over three years, we identify a very small subset (1642; 0.004\%) of individual reports describing personal experiences with HIV drug treatment. Results: Despite the small size of the extracted final dataset, the summary representation of adverse effects attributed to specific drugs, or drug combinations, accurately captures well-recognized toxicities. In addition, the data allowed us to discriminate across specific drug compounds, to identify preferred drugs over time, and to capture novel events such as the availability of preexposure prophylaxis. Conclusions: The effect of limited data sharing due to the public nature of the data can be partially offset by the large number of people sharing data in the first place, an observation that may play a key role in digital epidemiology in general. ", doi="10.2196/publichealth.4488", url="http://publichealth.jmir.org/2015/2/e7/", url="http://www.ncbi.nlm.nih.gov/pubmed/27227141" } @Article{info:doi/10.2196/jmir.4304, author="Lardon, J{\'e}r{\'e}my and Abdellaoui, Redhouane and Bellet, Florelle and Asfari, Hadyl and Souvignet, Julien and Texier, Nathalie and Jaulent, Marie-Christine and Beyens, Marie-No{\"e}lle and Burgun, Anita and Bousquet, C{\'e}dric", title="Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review", journal="J Med Internet Res", year="2015", month="Jul", day="10", volume="17", number="7", pages="e171", keywords="pharmacovigilance", keywords="adverse drug reaction", keywords="Internet", keywords="Web 2.0", keywords="social media", keywords="text mining", keywords="scoping review", keywords="adverse event", abstract="Background: The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. Objective: A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. Methods: Daubt et al's recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. Results: Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. Conclusions: This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system. ", doi="10.2196/jmir.4304", url="http://www.jmir.org/2015/7/e171/", url="http://www.ncbi.nlm.nih.gov/pubmed/26163365" } @Article{info:doi/10.2196/jmir.3962, author="Gottlieb, Assaf and Hoehndorf, Robert and Dumontier, Michel and Altman, B. Russ", title="Ranking Adverse Drug Reactions With Crowdsourcing", journal="J Med Internet Res", year="2015", month="Mar", day="23", volume="17", number="3", pages="e80", keywords="pharmacovigilance", keywords="adverse drug reactions", keywords="drug side effects", keywords="crowdsourcing", keywords="patient-centered care", keywords="alert fatigue", abstract="Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making. ", doi="10.2196/jmir.3962", url="http://www.jmir.org/2015/3/e80/", url="http://www.ncbi.nlm.nih.gov/pubmed/25800813" } @Article{info:doi/10.2196/medinform.3022, author="Polepalli Ramesh, Balaji and Belknap, M. Steven and Li, Zuofeng and Frid, Nadya and West, P. Dennis and Yu, Hong", title="Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration's Adverse Event Reporting System Narratives", journal="JMIR Med Inform", year="2014", month="Jun", day="27", volume="2", number="1", pages="e10", keywords="natural language processing", keywords="pharmacovigilance", keywords="adverse drug events", abstract="Background: The Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. Objective: The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. Methods: We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. Results: The annotated corpus had an agreement of over .9 Cohen's kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. Conclusions: In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance. ", doi="10.2196/medinform.3022", url="http://medinform.jmir.org/2014/1/e10/", url="http://www.ncbi.nlm.nih.gov/pubmed/25600332" } @Article{info:doi/10.2196/jmir.2614, author="Yom-Tov, Elad and Gabrilovich, Evgeniy", title="Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries", journal="J Med Internet Res", year="2013", month="Jun", day="18", volume="15", number="6", pages="e124", keywords="machine learning", keywords="information retrieval", keywords="side effects", keywords="infoveillance", keywords="infodemiology", abstract="Background: Postmarket drug safety surveillance largely depends on spontaneous reports by patients and health care providers; hence, less common adverse drug reactions---especially those caused by long-term exposure, multidrug treatments, or those specific to special populations---often elude discovery. Objective: Here we propose a low cost, fully automated method for continuous monitoring of adverse drug reactions in single drugs and in combinations thereof, and demonstrate the discovery of heretofore-unknown ones. Methods: We used aggregated search data of large populations of Internet users to extract information related to drugs and adverse reactions to them, and correlated these data over time. We further extended our method to identify adverse reactions to combinations of drugs. Results: We validated our method by showing high correlations of our findings with known adverse drug reactions (ADRs). However, although acute early-onset drug reactions are more likely to be reported to regulatory agencies, we show that less acute later-onset ones are better captured in Web search queries. Conclusions: Our method is advantageous in identifying previously unknown adverse drug reactions. These ADRs should be considered as candidates for further scrutiny by medical regulatory authorities, for example, through phase 4 trials. ", doi="10.2196/jmir.2614", url="http://www.jmir.org/2013/6/e124/", url="http://www.ncbi.nlm.nih.gov/pubmed/23778053" }