The World Health Organization defines pharmacovigilance (PV) as the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems [1]. In this era of what Edwards calls “information explosion,” we must rethink PV [2] to effectively incorporate a variety of data sources while ensuring the timely decision-making that is crucial to avoiding unnecessary harm caused by adverse events (AEs) in real-world health care practice.

Current PV activities depend heavily on voluntary, spontaneous AE reports obtained from health care professionals (HCPs). It is generally accepted that one advantage of spontaneous reporting is its speed at detecting AE signals as early as possible. However, it is also acknowledged that spontaneous reports by HCPs alone may not be enough to capture all AE signals in a timely fashion. Because some symptomatic AEs can be expected to be reported only by patients who have firsthand experience of drug treatment [3], incorporating patient-generated data into PV is one of the most important challenges [4]. Several studies have suggested that self-reporting by patients is useful for catching AE signals earlier, and many countries have implemented patient AE reporting schemes [5-8]. The Japanese regulatory authority started preliminary implementation of a self-reporting system for patients in March 2012 [9,10]; however, the system is still under development and will require more time to be used effectively in a routine PV system [11].

Analyzing information on the Internet would add significant knowledge about public health, as shown in Eysenbach’s study outlining the framework of infodemiology and infoveillance [12]. In PV, there has been recent growing interest in utilizing patient-generated Internet resources such as social media [13-17]. A survey conducted in 2001 and 2002 in the United States showed that the Internet is an important resource for the public; approximately 40% of respondents there obtained information on health-related topics through Internet sources [18]. In response to the increasing use of social media to share health care information, the US Food and Drug Administration announced in 2015 that they had started a collaboration with PatientsLikeMe [19], a patient networking website, to apply patient-generated data to risk management activities [20]. In Europe, the Medicines and Healthcare products Regulatory Agency in the United Kingdom started working with the WEB-RADR project in 2014 to develop a mobile phone app that helps HCPs and patients report AEs to national health care authorities [21]. The European Medicines Agency has also released guidelines on good pharmacovigilance practices, of which Module VI requires companies having the European Union marketing authorization to monitor the Internet or digital media under their management or responsibility for potential reports of suspected adverse reactions [22]. These ongoing efforts are expected to lead to important developments in PV. Like Americans and Europeans, approximately 39% of Japanese obtain health information via the Internet [23]. However, to our knowledge, no studies have explicitly identified such Japanese data sources for use in PV.

Our motivation was to take the first step toward enhancing PV by considering the application of patient-generated data sources in Japan. In this study, we focused on the potential use of health-related disease blogs called tōbyōki. The term tōbyōki translates literally to “an account of a struggle with disease,” and this form of writing predates the Internet. Although it is difficult to pinpoint when patients started writing tōbyōki, a sociological study has reported that the number of tōbyōki has been increasing in Japan since the 1970s [24]. In these diary-like accounts, patients record observations about their lives and diseases in handwritten journals. Recently, some patients have started sharing their tōbyōki as blogs on the Internet.

It has already been suggested that analyzing tōbyōki blogs is useful for understanding patients’ feelings when they receive a cancer diagnosis [25], although there was no discussion on their potential use in PV. In this study, we introduce a growing database called TOBYO, which is a collection of a broad range of tōbyōki blogs on the Internet [26]. The objective of this exploratory study was to address the following questions: (1) what kinds of data elements exist in the TOBYO database? (2) what are the differences in population distribution between the TOBYO database and other external databases generated by HCPs? (3) what kinds of analytic approaches are useful to obtain insights from the TOBYO database? and (4) can the TOBYO database be useful for PV?

To achieve our objective, we conducted 2 analyses (Analysis A and Analysis B). In Analysis A, we used the whole TOBYO database to describe data elements and understand the overall characteristics of this database. In Analysis B, we used a data subset of selected disorders from the TOBYO database to explore the usefulness of the database in greater detail. Here, we focused on depressive disorders and rheumatoid arthritis (RA) because these conditions were expected to entail subjective patient symptoms such as discomfort, insomnia, and pain. Finally, we included a discussion of the potential of the TOBYO database and practical challenges from the PV perspective.

In this study, we considered health-related tōbyōki blogs as a resource for patient-generated data. Some examples of excerpts from tōbyōki blogs are shown in Table 1. As shown in these examples, patients shared information about AEs, drug name, dosage, and AE-related distress.

The TOBYO database consisted of a Web-based collection of tōbyōki blogs written in Japanese [26] and maintained by Initiative Inc (Tokyo, Japan). The overall flow of data in the TOBYO database is shown in Figure 1. Blogs written in Japanese were identified and extracted daily from the Internet using a proprietary crawling method. Before being registered in the TOBYO database, each tōbyōki blog was manually checked to judge whether it was a tōbyōki blog or noise, which was excluded. Each blog registered to the TOBYO database met all of the following selection criteria: (1) Language　criteria: blogs written in plain Japanese language without extensive use of emoticons, symbols, or colloquial expressions were included; (2) Blogger criteria: blogs written by patients or their families were included. Blogs not written by patients or their families, such as those by manufacturers or HCPs who were providing medical care, were excluded (because such blogs generally described the HCP’s records and did not contain a patient perspective); and (3) Content criteria: blogs containing at least ten pages of tōbyōki entries on patients’ actual experiences were included. Blogs comprising excerpts from news media, books, health-related websites, or treatment guidelines were excluded. Blogs intended for marketing or promotion of commercial services or religious or political beliefs were also excluded.

At the time of registration in the TOBYO database, information on gender, age at onset, and the primary disorder of each patient was determined by checking the profile or introduction page of each tōbyōki blog and stored as structured data for each patient. Text-based data in tōbyōki blogs were stored as unstructured data for each patient.

The study protocol was reviewed and approved by the nonprofit MINS Institutional Review Board [35]. The board waived informed consent because the data source did not contain personal information. In addition, we presented the data at the group level rather than at the individual level.

Patient-generated data is likely to play a key role in improving PV [36]. In Japan, however, a system of self-reporting by patients is still being considered [10] and no patient-generated data resources have been explicitly identified. As one option for such a resource, this study evaluated the TOBYO database from the PV perspective.

In the whole TOBYO database, more blogs were written by female bloggers, and fewer blogs were written by people older than 50 years (Table 2). These findings were consistent with the results of a general survey of Internet usage in Japan [23]. Reflecting the fact that a higher percentage of tōbyōki blogs were written by women, the most frequently appearing disorders in the TOBYO database tended to have a higher prevalence in women: breast cancer, cervical cancer [37], RA [38], and panic disorder [39] (Table 3). Additional analysis of tōbyōki blogs would be more realistic for these disorders with a high prevalence in women. Our findings also suggested the relevance to frequently appearing disorders such as psychiatric disorders with subjective symptoms that have important clinical meaning, refractory disorders, autoimmune disorders, and other chronic disorders.

As shown in Tables 5 and 6, tōbyōki blogs written by patients with depression or RA contained symptomatic, subjective terms rather than the medical diagnosis or other medical terms. This revealed a difference between tōbyōki blogs and the JADER database generated by HCPs and implied that the TOBYO database might have the advantage of enabling the analysis of patient-level outcomes that could not be captured in existing data sources. Indeed, previous research has shown that psychiatric events are difficult to identify in health care administrative databases because physicians have difficulty detecting them and patients avoid reporting the symptoms to their physicians [40]. Another interesting possibility is that even if a patient reporting system is implemented, patients may not voluntarily report events that they do not consider to be AEs, as suggested by a previous research conducted on patients with Parkinson’s disease [41]. In such a situation, in which patients themselves do not consider the possibility of AEs, the TOBYO database can be useful for capturing initial symptoms as AE signals.

We confirmed the feasibility of analyzing patient narratives using text mining to draw insights from tōbyōki blogs. Word clouds suggested characteristic words associated with selected conditions, such as “sleeping” and “anxiety” with depression and “pain” and “painful” with RA. This suggested that tōbyōki blogs were a useful resource for understanding characteristic information for each disorder. We were also able to identify words commonly associated with the 4 biological drugs located at the center of the word cooccurrence networks (Figure 8). The common words revealed in this study were not particularly noteworthy, but further research using the same approach with different drugs or disease areas might be useful for exploring drug safety concerns such as unknown AEs. For example, a report analyzing tweets written by Japanese patients with cancer suggested that visualizing narratives with word cooccurrence networks could be a useful approach to obtain insights from social media [42].

We noted several strengths of tōbyōki blogs as a resource for data analysis in this study. One was the ease of obtaining patient background information as summarized in Table 2. In contrast to other data sources such as Web-based discussion forums in which patient background information was inherently limited [43], tōbyōki blogs usually had a profile or introduction page from which a substantial level of information could be collected. Another strength was that most tōbyōki bloggers wrote their blogs voluntarily to record and share their experiences with others, resulting in primarily subjective descriptions of patient experiences. This first-hand, observational quality, free from obligations or interventions, might enable researchers to better understand patients’ actual concerns. A third strength was that compared with common blogs or social media (even those written by patients), tōbyōki blogs might be more likely to contain analyzable information on health-related or life-related topics because serious disease and other health crises were typical motivations for starting tōbyōki blogs.

This study had several limitations. First, because tōbyōki blogs were written by only a segment of the patient population, generalization of the findings required caution. For instance, the elderly population might be underrepresented in Internet sources [23]. In addition, as a patient’s condition became more severe, it might be more difficult for them to continue writing their tōbyōki blogs. These biases should be considered when interpreting the results. Second, the insights obtained from qualitative text-mining approaches were based on some degree of subjective interpretation by researchers. For example, in word clouds, the relative size of each word reflected its frequency. It would be helpful to identify frequent or important words that were mentioned by many bloggers. On the other hand, because the size of each word did not reflect its clinical significance, it was possible that some smaller words might have greater clinical significances. Although word clouds have the potential to provide some insights from textual data, interpretation should be done in caution, keeping their pros and cons in mind. Third, some technical improvements would be necessary to extract more meaningful knowledge from the texts used in this study. For instance, we only considered fragmented words for analysis. By excluding phrases and other word combinations, we might have missed some important concepts or patient feelings. Additional techniques such as entity linking or named entity recognition should be considered in future studies to improve the results. Finally, because the language in social media tends to be highly informal and contain a wide variety of expressions, identification of specific concepts such as AEs and medicinal drugs from the unstructured narratives is a challenge. Although we could identify frequently appearing medical events in the TOBYO database, as shown in Tables 5 and 6, it is apparent that not all these events were AEs because we did not consider whether they had occurred before or after drug administration. Additional work is necessary to identify AEs occurring after drug administration.

We also recognized future challenges for the effective use of social media data in PV. First, there is a need for an official guidance or policy about the necessity of obtaining informed consent from patients and protecting privacy. Although research interest in the use of social media is growing, there is currently no consensus or guideline [44]. We think there is no need for artificial constraints such as obtaining subsequent informed consent for the use of blog data because they are already publicly available on the Internet. Regarding patients’ decisions on whether to share data, a study showed that patients in the cancer community tended to think positively about sharing as long as the benefit of sharing data outweighed the risk [45], and the authors recommended that researchers should be careful to protect patient anonymity. In accordance with this recommendation, we prepared all analysis output as summarized data and not individual-level data in consideration of patients’ rights to protected privacy. Second, we acknowledge that issues exist with the reliability and reproducibility of social media, particularly from the regulatory, good pharmacovigilance practice perspective. Concerns about the incorporation of false information have been noted previously [46]. Considering our study using tōbyōki blogs, we assume that the extent of this problem would not be very large because there is no conceivable incentive for maintaining a fake tōbyōki blog at this time. Selecting blogs with more than 10 pages in the screening process before registration in the TOBYO database would help to prevent the inclusion of fake blogs. Concerns about the reproducibility of analysis present a practical challenge. It is not realistic to keep a dynamic dataset that is updated every day and that may be updated retrospectively. To ensure the reproducibility of individual research, storing the final dataset as a snapshot is recommended. Finally, because the volume of data on the Internet is continuously growing, there may be a need to think about how to efficiently detect and process AE information on the Internet. One option is to improve the text-mining algorithm using dictionary-based methods by preparing an annotated corpus to recognize AEs and drugs. However, this process would be time-consuming and costly. Another option is the application of a machine learning approach by preparing a classifier algorithm that does not necessarily require the preparation of annotated corpora, and there has been a report of the application of a deep-learning technique to detect potential AEs from social media texts [47]. In summary, we need to tackle several practical and technical issues to efficiently incorporate social media resources into PV.

PV activities should have a strong focus on patients’ actual experiences, concerns, and outcomes, and this approach is expected to uncover hidden AE signals earlier and help us understand AEs in a patient-centered way. This study described the fundamental characteristics of tōbyōki blogs in the TOBYO database and provided insights into considering the use of such data for PV. Specific application possibilities for the TOBYO database include the analysis of disorders with a high prevalence in women, psychiatric disorders with important subjective symptoms, refractory disorders, and other chronic disorders. Further research would facilitate the enhancement of PV by incorporating patient-generated data from the Internet.