Search Articles

View query in Help articles search

Search Results (1 to 10 of 16 Results)

Download search results: CSV END BibTex RIS


Enhancing Care Coordination in Oncology and Nononcology Thoracic Surgery Care Pathways Through a Digital Health Solution: Mixed Methods Study

Enhancing Care Coordination in Oncology and Nononcology Thoracic Surgery Care Pathways Through a Digital Health Solution: Mixed Methods Study

In the first stage, based on the interviews conducted face-to-face under objective 1, we were able to (1) identify the clinical and administrative needs of future users and (2) map the current thoracic surgery care pathways to identify optimization opportunities and implement targeted improvements. This information was gathered from the following 13 participants: 5 (38%) MUHC surgeons, 3 (23%) CISSSO nurses, 3 (23%) MUHC nurses, and 2 (15%) MUHC medical secretaries.

Véronique Nabelsi, Véronique Plouffe

JMIR Form Res 2024;8:e60222

Evaluating Artificial Intelligence in Clinical Settings—Let Us Not Reinvent the Wheel

Evaluating Artificial Intelligence in Clinical Settings—Let Us Not Reinvent the Wheel

This is largely because developers and implementers focus on tool development and do not sufficiently draw on existing work to inform the conception and design of technologies, their use and optimization, and organizational strategies to implement them. Theory-informed approaches to evaluation can help to ensure that technologies are effectively validated, implemented, and adopted.

Kathrin Cresswell, Nicolette de Keizer, Farah Magrabi, Robin Williams, Michael Rigby, Mirela Prgomet, Polina Kukhareva, Zoie Shui-Yee Wong, Philip Scott, Catherine K Craven, Andrew Georgiou, Stephanie Medlock, Jytte Brender McNair, Elske Ammenwerth

J Med Internet Res 2024;26:e46407

Development of an Artificial Intelligence–Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial

Development of an Artificial Intelligence–Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial

After the pilot test, we posted advertisements on 2 large web-based nursing communities between September 2022 and February 2023 to recruit participants for AI algorithm optimization. The number of participants required for measuring the intervention effect was calculated based on the paired 2-tailed t test for measuring the effect of burnout, a research variable of this study.

Aram Cho, Chiyoung Cha, Gumhee Baek

J Med Internet Res 2024;26:e54029

Optimization of Screening Strategies for COVID-19: Scoping Review

Optimization of Screening Strategies for COVID-19: Scoping Review

We abstracted data on the last name of the first author, research design, research population, optimization design, testing method, screening strategy, evaluation index, and recommendation. All authors participated in the data abstraction and reconfirmation of the abstraction. YL and YY charted the data, grouped the studies according to the optimization directions of the screening strategies, and summarized the findings.

Yuanhua Liu, Yun Yin, Michael P Ward, Ke Li, Yue Chen, Mengwei Duan, Paulina P Y Wong, Jie Hong, Jiaqi Huang, Jin Shi, Xuan Zhou, Xi Chen, Jiayao Xu, Rui Yuan, Lingcai Kong, Zhijie Zhang

JMIR Public Health Surveill 2024;10:e44349

Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study

Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study

We designed an optimization function to identify meals with optimal nutrition value. To integrate users’ other preferences, we proposed a novel, multicriteria decision analysis mechanism assisted by a heuristic search method that can efficiently locate meals that satisfy multiple, conflicting user preferences. In the following sections, we’ve presented each of the system components. Proposed planning system architecture. The brain of the planner is a comprehensive food and nutrition knowledge graph [21].

Maryam Amiri, Juan Li, Wordh Hasan

JMIR Form Res 2023;7:e46434

Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions

Supporting Adolescent Engagement with Artificial Intelligence–Driven Digital Health Behavior Change Interventions

The AIM-EEE framework outlines 4 key roles for AI in DHBCIs for adolescent engagement, using measurement, modeling, optimization, and generation. The framework is situated in a call for increased attention to crucial ethical concerns that arise when using AI technologies, especially those related to privacy, algorithmic fairness, transparency, and accountability.

Alison Giovanelli, Jonathan Rowe, Madelynn Taylor, Mark Berna, Kathleen P Tebb, Carlos Penilla, Marianne Pugatch, James Lester, Elizabeth M Ozer

J Med Internet Res 2023;25:e40306

Introduction of a Single Electronic Health Record for Maternity Units in Ireland: Outline of the Experiences of the Project Management Team

Introduction of a Single Electronic Health Record for Maternity Units in Ireland: Outline of the Experiences of the Project Management Team

Many complex steps have been identified in the implementation of an EHR from procurement, design, building, testing, and training through to adoption and optimization [5]. A hospital-wide EHR implementation needs to consider a number of organizational, human, and technological factors as well as organizational structure, culture, technical infrastructure, financial resources, and coordination [4,5].

Orla Maria Sheehan, Richard Anthony Greene, Joye McKernan, Brendan Murphy, Caroline Cahill, Brian Cleary, Fiona Lawlor, Michael Robson, MN-CMS National Project Team

JMIR Form Res 2023;7:e38938

Testing and Optimizing Guided Thinking Tasks to Promote Physical Activity: Protocol for a Randomized Factorial Trial

Testing and Optimizing Guided Thinking Tasks to Promote Physical Activity: Protocol for a Randomized Factorial Trial

Identifying active and inert intervention components in early-phase development (versus later on in the process) is a means to build more efficient and scalable interventions and is aligned with current intervention development frameworks, such as the Multiphase Optimization Strategy (MOST) [15], Obesity-Related Behavioral Intervention Trials (ORBIT) model [20], and Science of Behavior Change [21].

Austin S Baldwin, Colin L Lamb, Bree A Geary, Alexis D Mitchell, Chrystyna D Kouros, Sara Levens, Laura E Martin

JMIR Res Protoc 2022;11(9):e40908