@Article{info:doi/10.2196/52675, author="Willcockson, Ursula Irmgard and Valdes, Herman Ignacio", title="Unintended Consequences of Data Sharing Under the Meaningful Use Program", journal="JMIR Med Inform", year="2024", month="Nov", day="14", volume="12", pages="e52675", keywords="electronic health records", keywords="EHR", keywords="medical record", keywords="interoperability", keywords="health information interoperability", keywords="clinical burden", keywords="Medicare", keywords="Medicaid", keywords="reimbursement", keywords="data science", keywords="data governance", keywords="data breach", keywords="cybersecurity", keywords="privacy", doi="10.2196/52675", url="https://medinform.jmir.org/2024/1/e52675" } @Article{info:doi/10.2196/64226, author="Nagarajan, Radha and Kondo, Midori and Salas, Franz and Sezgin, Emre and Yao, Yuan and Klotzman, Vanessa and Godambe, A. Sandip and Khan, Naqi and Limon, Alfonso and Stephenson, Graham and Taraman, Sharief and Walton, Nephi and Ehwerhemuepha, Louis and Pandit, Jay and Pandita, Deepti and Weiss, Michael and Golden, Charles and Gold, Adam and Henderson, John and Shippy, Angela and Celi, Anthony Leo and Hogan, R. William and Oermann, K. Eric and Sanger, Terence and Martel, Steven", title="Economics and Equity of Large Language Models: Health Care Perspective", journal="J Med Internet Res", year="2024", month="Nov", day="14", volume="26", pages="e64226", keywords="large language model", keywords="LLM", keywords="health care", keywords="economics", keywords="equity", keywords="cloud service providers", keywords="cloud", keywords="health outcome", keywords="implementation", keywords="democratization", doi="10.2196/64226", url="https://www.jmir.org/2024/1/e64226" } @Article{info:doi/10.2196/57035, author="van Maurik, S. I. and Doodeman, J. H. and Veeger-Nuijens, W. B. and M{\"o}hringer, M. R. P. and Sudiono, R. D. and Jongbloed, W. and van Soelen, E.", title="Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data", journal="JMIR Med Inform", year="2024", month="Oct", day="24", volume="12", pages="e57035", keywords="clinical prediction model", keywords="electronic health record", keywords="targeted validation", keywords="EHR", keywords="EMR", keywords="prediction models", keywords="validation", keywords="CPM", keywords="secondary care", keywords="machine learning", keywords="artificial intelligence", keywords="AI", doi="10.2196/57035", url="https://medinform.jmir.org/2024/1/e57035" } @Article{info:doi/10.2196/60402, author="Fernando, Manasha and Abell, Bridget and McPhail, M. Steven and Tyack, Zephanie and Tariq, Amina and Naicker, Sundresan", title="Applying the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability Framework Across Implementation Stages to Identify Key Strategies to Facilitate Clinical Decision Support System Integration Within a Large Metropolitan Health Service: Interview and Focus Group Study", journal="JMIR Med Inform", year="2024", month="Oct", day="17", volume="12", pages="e60402", keywords="medical informatics", keywords="adoption and implementation", keywords="behavior", keywords="health systems", abstract="Background: Computerized clinical decision support systems (CDSSs) enhance patient care through real-time, evidence-based guidance for health care professionals. Despite this, the effective implementation of these systems for health services presents multifaceted challenges, leading to inappropriate use and abandonment over the course of time. Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework, this qualitative study examined CDSS adoption in a metropolitan health service, identifying determinants across implementation stages to optimize CDSS integration into health care practice. Objective: This study aims to identify the theory-informed (NASSS) determinants, which included multiple CDSS interventions across a 2-year period, both at the health-service level and at the individual hospital setting, that either facilitate or hinder the application of CDSSs within a metropolitan health service. In addition, this study aimed to map these determinants onto specific stages of the implementation process, thereby developing a system-level understanding of CDSS application across implementation stages. Methods: Participants involved in various stages of the implementation process were recruited (N=30). Participants took part in interviews and focus groups. We used a hybrid inductive-deductive qualitative content analysis and a framework mapping approach to categorize findings into barriers, enablers, or neutral determinants aligned to NASSS framework domains. These determinants were also mapped to implementation stages using the Active Implementation Framework stages approach. Results: Participants comprised clinical adopters (14/30, 47\%), organizational champions (5/30, 16\%), and those with roles in organizational clinical informatics (5/30, 16\%). Most determinants were mapped to the organization level, technology, and adopter subdomains. However, the study findings also demonstrated a relative lack of long-term implementation planning. Consequently, determinants were not uniformly distributed across the stages of implementation, with 61.1\% (77/126) identified in the exploration stage, 30.9\% (39/126) in the full implementation stage, and 4.7\% (6/126) in the installation stages. Stakeholders engaged in more preimplementation and full-scale implementation activities, with fewer cycles of monitoring and iteration activities identified. Conclusions: These findings addressed a substantial knowledge gap in the literature using systems thinking principles to identify the interdependent dynamics of CDSS implementation. A lack of sustained implementation strategies (ie, training and longer-term, adopter-level championing) weakened the sociotechnical network between developers and adopters, leading to communication barriers. More rigorous implementation planning, encompassing all 4 implementation stages, may, in a way, help in addressing the barriers identified and enhancing enablers. ", doi="10.2196/60402", url="https://medinform.jmir.org/2024/1/e60402" } @Article{info:doi/10.2196/58478, author="Reis, Florian and Lenz, Christian and Gossen, Manfred and Volk, Hans-Dieter and Drzeniek, Michael Norman", title="Practical Applications of Large Language Models for Health Care Professionals and Scientists", journal="JMIR Med Inform", year="2024", month="Sep", day="5", volume="12", pages="e58478", keywords="artificial intelligence", keywords="healthcare", keywords="chatGPT", keywords="large language model", keywords="prompting", keywords="LLM", keywords="applications", keywords="AI", keywords="scientists", keywords="physicians", keywords="health care", doi="10.2196/58478", url="https://medinform.jmir.org/2024/1/e58478" } @Article{info:doi/10.2196/58080, author="Svempe, Liga", title="Exploring Impediments Imposed by the Medical Device Regulation EU 2017/745 on Software as a Medical Device", journal="JMIR Med Inform", year="2024", month="Sep", day="5", volume="12", pages="e58080", keywords="software", keywords="artificial intelligence", keywords="medical device regulation", keywords="rights", keywords="digital health", doi="10.2196/58080", url="https://medinform.jmir.org/2024/1/e58080", url="http://www.ncbi.nlm.nih.gov/pubmed/39235850" } @Article{info:doi/10.2196/58548, author="Julian, Silva Guilherme and Shau, Wen-Yi and Chou, Hsu-Wen and Setia, Sajita", title="Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries", journal="JMIR Med Inform", year="2024", month="Aug", day="15", volume="12", pages="e58548", keywords="Asia", keywords="electronic medical records", keywords="EMR", keywords="health care databases", keywords="health technology assessment", keywords="HTA", keywords="real-world data", keywords="real-world evidence", doi="10.2196/58548", url="https://medinform.jmir.org/2024/1/e58548", url="http://www.ncbi.nlm.nih.gov/pubmed/39026427" } @Article{info:doi/10.2196/59005, author="Sendra-Portero, Francisco and Lorenzo-{\'A}lvarez, Roc{\'i}o and Rudolphi-Solero, Teodoro and Ruiz-G{\'o}mez, Jos{\'e} Miguel", title="The Second Life Metaverse and Its Usefulness in Medical Education After a Quarter of a Century", journal="J Med Internet Res", year="2024", month="Aug", day="6", volume="26", pages="e59005", keywords="medical education", keywords="medical students", keywords="postgraduate", keywords="computer simulation", keywords="virtual worlds", keywords="metaverse", doi="10.2196/59005", url="https://www.jmir.org/2024/1/e59005" } @Article{info:doi/10.2196/50355, author="Han Sr, Wei and Li 2nd, Yuanting and Chen 3rd, Changgen and Huang, Danni and Wang, Junchao and Li, Xiang and Ji, Zhongliang and Li, Qin and Li, Zhuang", title="5G Key Technologies for Helicopter Aviation Medical Rescue", journal="J Med Internet Res", year="2024", month="Aug", day="1", volume="26", pages="e50355", keywords="low airspace", keywords="helicopters", keywords="medical aid", keywords="5G technology", keywords="aeronautical engineering", doi="10.2196/50355", url="https://www.jmir.org/2024/1/e50355" } @Article{info:doi/10.2196/55933, author="Zhui, Li and Yhap, Nina and Liping, Liu and Zhengjie, Wang and Zhonghao, Xiong and Xiaoshu, Yuan and Hong, Cui and Xuexiu, Liu and Wei, Ren", title="Impact of Large Language Models on Medical Education and Teaching Adaptations", journal="JMIR Med Inform", year="2024", month="Jul", day="25", volume="12", pages="e55933", keywords="large language models", keywords="medical education", keywords="opportunities", keywords="challenges", keywords="critical thinking", keywords="educator", doi="10.2196/55933", url="https://medinform.jmir.org/2024/1/e55933" } @Article{info:doi/10.2196/60116, author="Chindamorragot, Naruemol and Suitthimeathegorn, Orawan and Garg, Amit", title="Centralized Pump Monitoring System: Perception on Utility and Workflows by Nurses in a Tertiary Hospital", journal="Asian Pac Isl Nurs J", year="2024", month="Jul", day="24", volume="8", pages="e60116", keywords="infusion management", keywords="nurse efficiency", keywords="pump monitoring system", keywords="nurse attrition", doi="10.2196/60116", url="https://apinj.jmir.org/2024/1/e60116" } @Article{info:doi/10.2196/54951, author="Lakew, Nathan and Jonsson, Jakob and Lindner, Philip", title="Probing the Role of Digital Payment Solutions in Gambling Behavior: Preliminary Results From an Exploratory Focus Group Session With Problem Gamblers", journal="JMIR Hum Factors", year="2024", month="Jul", day="23", volume="11", pages="e54951", keywords="digital payment solutions", keywords="online gambling behavior", keywords="sociotechnical", keywords="subjective experience", keywords="focus group", abstract="Background: Technology has significantly reshaped the landscape and accessibility of gambling, creating uncharted territory for researchers and policy makers involved in the responsible gambling (RG) agenda. Digital payment solutions (DPS) are the latest addition of technology-based services in gambling and are now prominently used for deposit and win withdrawal. The seamless collaboration between online gambling operators and DPS, however, has raised concerns regarding the potential role of DPS platforms in facilitating harmful behavior. Objective: Using a focus group session with problem gamblers, this study describes a preliminary investigation of the role of DPS in the online gambling context and its influence on players' gambling habits, financial behavior, choices of gambling environment, and the overall outcome of gambling subjective experiences. Methods: A total of 6 problem gamblers participated in a one-and-half-hour focus group session to discuss how DPSs are integrated into their everyday gambling habits, what motivates them to use DPS, and what shifts they observe in their gambling behavior. Thematic analysis was used to analyze the empirical evidence with a mix of inductive and deductive research approaches as a knowledge claim strategy. Results: Our initial findings revealed that the influence of DPSs in online gambling is multifaced where, on the one hand, their ability to integrate with players' existing habits seamlessly underscores the facilitating role they play in potentially maximizing harm. On the other hand, we find preliminary evidence that DPSs can have a direct influence on gambling outcomes in both subtle and pervasive ways---nudging, institutionalizing, constraining, or triggering players' gambling activities. This study also highlights the increasingly interdisciplinary nature of online gambling, and it proposes a preliminary conceptual framework to illustrate the sociotechnical interplay between DPS and gambling habits that ultimately capture the outcome of gambling's subjective experience. Conclusions: Disguised as a passive payment enabler, the role of DPS has so far received scant attention; however, this exploratory qualitative study demonstrates that given the technological advantage and access to customer financial data, DPS can become a potent platform to enable and at times trigger harmful gambling. In addition, DPS's bird's-eye view of cross-operator gambling behavior can open up an opportunity for researchers and policy makers to explore harm reduction measures that can be implemented at the digital payment level for gambling customers. Finally, more interdisciplinary studies are needed to formulate the sociotechnical nature of online gambling and holistic harm minimization strategy. ", doi="10.2196/54951", url="https://humanfactors.jmir.org/2024/1/e54951" } @Article{info:doi/10.2196/54590, author="Lamer, Antoine and Saint-Dizier, Chlo{\'e} and Paris, Nicolas and Chazard, Emmanuel", title="Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline", journal="JMIR Med Inform", year="2024", month="Jul", day="17", volume="12", pages="e54590", keywords="data reuse", keywords="data lake", keywords="data warehouse", keywords="feature extraction", keywords="datamart", keywords="feature store", doi="10.2196/54590", url="https://medinform.jmir.org/2024/1/e54590" } @Article{info:doi/10.2196/50437, author="Faust, Louis and Wilson, Patrick and Asai, Shusaku and Fu, Sunyang and Liu, Hongfang and Ruan, Xiaoyang and Storlie, Curt", title="Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice", journal="JMIR Med Inform", year="2024", month="Jun", day="28", volume="12", pages="e50437", keywords="artificial intelligence", keywords="machine learning", keywords="implementation science", keywords="quality control", keywords="monitoring", keywords="patient safety", doi="10.2196/50437", url="https://medinform.jmir.org/2024/1/e50437", url="http://www.ncbi.nlm.nih.gov/pubmed/38941140" } @Article{info:doi/10.2196/58491, author="Lu, Linken and Lu, Tangsheng and Tian, Chunyu and Zhang, Xiujun", title="AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine", journal="JMIR Med Inform", year="2024", month="Jun", day="28", volume="12", pages="e58491", keywords="traditional Chinese medicine", keywords="TCM", keywords="artificial intelligence", keywords="AI", keywords="diagnosis", doi="10.2196/58491", url="https://medinform.jmir.org/2024/1/e58491", url="http://www.ncbi.nlm.nih.gov/pubmed/38941141" } @Article{info:doi/10.2196/51350, author="Richter, Gesine and Krawczak, Michael", title="How to Elucidate Consent-Free Research Use of Medical Data: A Case for ``Health Data Literacy''", journal="JMIR Med Inform", year="2024", month="Jun", day="18", volume="12", pages="e51350", keywords="health data literacy", keywords="informed consent", keywords="broad consent", keywords="data sharing", keywords="data collection", keywords="data donation", keywords="data linkage", keywords="personal health data", doi="10.2196/51350", url="https://medinform.jmir.org/2024/1/e51350" } @Article{info:doi/10.2196/56572, author="Nkoy, L. Flory and Stone, L. Bryan and Zhang, Yue and Luo, Gang", title="A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection", journal="JMIR Med Inform", year="2024", month="Apr", day="17", volume="12", pages="e56572", keywords="asthma", keywords="causal inference", keywords="forecasting", keywords="machine learning", keywords="decision support", 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="medication selection", keywords="respiratory", keywords="pulmonary", keywords="forecast", keywords="ICS", keywords="inhaled corticosteroid", keywords="inhaler", keywords="inhaled", keywords="corticosteroid", keywords="corticosteroids", keywords="artificial intelligence", keywords="personalized", keywords="customized", doi="10.2196/56572", url="https://medinform.jmir.org/2024/1/e56572", url="http://www.ncbi.nlm.nih.gov/pubmed/38630536" } @Article{info:doi/10.2196/55499, author="Asgari, Elham and Kaur, Japsimar and Nuredini, Gani and Balloch, Jasmine and Taylor, M. Andrew and Sebire, Neil and Robinson, Robert and Peters, Catherine and Sridharan, Shankar and Pimenta, Dominic", title="Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review", journal="JMIR Med Inform", year="2024", month="Apr", day="12", volume="12", pages="e55499", keywords="electronic health record", keywords="cognitive load", keywords="burnout", keywords="technology", keywords="clinician", doi="10.2196/55499", url="https://medinform.jmir.org/2024/1/e55499", url="http://www.ncbi.nlm.nih.gov/pubmed/38607672" } @Article{info:doi/10.2196/51138, author="Washington, Peter", title="A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health", journal="J Med Internet Res", year="2024", month="Apr", day="11", volume="26", pages="e51138", keywords="crowdsourcing", keywords="digital medicine", keywords="human-in-the-loop", keywords="human in the loop", keywords="human-AI collaboration", keywords="machine learning", keywords="precision health", keywords="artificial intelligence", keywords="AI", doi="10.2196/51138", url="https://www.jmir.org/2024/1/e51138", url="http://www.ncbi.nlm.nih.gov/pubmed/38602750" } @Article{info:doi/10.2196/51267, author="Karadag, Serap Ayse and Kandi, Basak and Sanl?, Berna and Ulusal, Hande and Basusta, Hasan and Sener, Seray and Cal?ka, Sinem", title="Social Media Use in Dermatology in Turkey: Challenges and Tips for Patient Health", journal="JMIR Dermatol", year="2024", month="Mar", day="28", volume="7", pages="e51267", keywords="social media", keywords="dermatology", keywords="internet", keywords="health promotion", keywords="patient education", keywords="Instagram", keywords="YouTube", keywords="online social networking", keywords="social networking", keywords="Turkey", keywords="patient health", keywords="skin", keywords="skin disease", keywords="skincare", keywords="cosmetics", keywords="digital communication", keywords="misinformation", doi="10.2196/51267", url="https://derma.jmir.org/2024/1/e51267", url="http://www.ncbi.nlm.nih.gov/pubmed/38546714" } @Article{info:doi/10.2196/49208, author="Kim, Meelim and Patrick, Kevin and Nebeker, Camille and Godino, Job and Stein, Spencer and Klasnja, Predrag and Perski, Olga and Viglione, Clare and Coleman, Aaron and Hekler, Eric", title="The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring", journal="J Med Internet Res", year="2024", month="Mar", day="5", volume="26", pages="e49208", keywords="accessible", keywords="decision making", keywords="decision", keywords="decision-based evidence-making", keywords="development", keywords="digital therapeutics", keywords="medication adherence", keywords="monitoring", keywords="pharmaceuticals", keywords="public health", keywords="real-world data", keywords="real-world evidence", keywords="safe", keywords="testing", keywords="therapeutics", doi="10.2196/49208", url="https://www.jmir.org/2024/1/e49208", url="http://www.ncbi.nlm.nih.gov/pubmed/38441954" } @Article{info:doi/10.2196/49022, author="Bhargava, Hansa and Salomon, Carmela and Suresh, Srinivasan and Chang, Anthony and Kilian, Rachel and Stijn, van Diana and Oriol, Albert and Low, Daniel and Knebel, Ashley and Taraman, Sharief", title="Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics", journal="J Med Internet Res", year="2024", month="Feb", day="29", volume="26", pages="e49022", keywords="artificial intelligence", keywords="pediatrics", keywords="autism spectrum disorder", keywords="ASD", keywords="disparities", keywords="pediatric", keywords="youth", keywords="child", keywords="children", keywords="autism", keywords="autistic", keywords="barrier", keywords="barriers", keywords="clinical application", keywords="clinical applications", keywords="professional development", keywords="continuing education", keywords="continuing medical education", keywords="CME", keywords="implementation", doi="10.2196/49022", url="https://www.jmir.org/2024/1/e49022", url="http://www.ncbi.nlm.nih.gov/pubmed/38421690" } @Article{info:doi/10.2196/41670, author="Stendal, Karen and Bernabe, C. Rosemarie D. L.", title="Extended Reality---New Opportunity for People With Disability? Practical and Ethical Considerations", journal="J Med Internet Res", year="2024", month="Feb", day="13", volume="26", pages="e41670", keywords="extended reality", keywords="virtual worlds", keywords="virtual reality", keywords="disability", keywords="practical", keywords="ethical", keywords="technology", keywords="virtual", keywords="reality", keywords="development", keywords="research", keywords="challenges", doi="10.2196/41670", url="https://www.jmir.org/2024/1/e41670", url="http://www.ncbi.nlm.nih.gov/pubmed/38349731" } @Article{info:doi/10.2196/51980, author="Sharma, Yashoda and Saha, Anindita and Goldsack, C. Jennifer", title="Defining the Dimensions of Diversity to Promote Inclusion in the Digital Era of Health Care: A Lexicon", journal="JMIR Public Health Surveill", year="2024", month="Feb", day="9", volume="10", pages="e51980", keywords="digital medicine", keywords="inclusion", keywords="digital health technology/product", keywords="digital health", keywords="digital technology", keywords="health care system", keywords="innovation", keywords="equity", keywords="quality", keywords="disparity", keywords="digital era", keywords="digital access", keywords="digital literacy", doi="10.2196/51980", url="https://publichealth.jmir.org/2024/1/e51980", url="http://www.ncbi.nlm.nih.gov/pubmed/38335013" } @Article{info:doi/10.2196/52080, author="Xu, Jian", title="The Current Status and Promotional Strategies for Cloud Migration of Hospital Information Systems in China: Strengths, Weaknesses, Opportunities, and Threats Analysis", journal="JMIR Med Inform", year="2024", month="Feb", day="5", volume="12", pages="e52080", keywords="hospital information system", keywords="HIS", keywords="cloud computing", keywords="cloud migration", keywords="Strengths, Weaknesses, Opportunities, and Threats analysis", abstract="Background: In the 21st century, Chinese hospitals have witnessed innovative medical business models, such as online diagnosis and treatment, cross-regional multidepartment consultation, and real-time sharing of medical test results, that surpass traditional hospital information systems (HISs). The introduction of cloud computing provides an excellent opportunity for hospitals to address these challenges. However, there is currently no comprehensive research assessing the cloud migration of HISs in China. This lack may hinder the widespread adoption and secure implementation of cloud computing in hospitals. Objective: The objective of this study is to comprehensively assess external and internal factors influencing the cloud migration of HISs in China and propose promotional strategies. Methods: Academic articles from January 1, 2007, to February 21, 2023, on the topic were searched in PubMed and HuiyiMd databases, and relevant documents such as national policy documents, white papers, and survey reports were collected from authoritative sources for analysis. A systematic assessment of factors influencing cloud migration of HISs in China was conducted by combining a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and literature review methods. Then, various promotional strategies based on different combinations of external and internal factors were proposed. Results: After conducting a thorough search and review, this study included 94 academic articles and 37 relevant documents. The analysis of these documents reveals the increasing application of and research on cloud computing in Chinese hospitals, and that it has expanded to 22 disciplinary domains. However, more than half (n=49, 52\%) of the documents primarily focused on task-specific cloud-based systems in hospitals, while only 22\% (n=21 articles) discussed integrated cloud platforms shared across the entire hospital, medical alliance, or region. The SWOT analysis showed that cloud computing adoption in Chinese hospitals benefits from policy support, capital investment, and social demand for new technology. However, it also faces threats like loss of digital sovereignty, supplier competition, cyber risks, and insufficient supervision. Factors driving cloud migration for HISs include medical big data analytics and use, interdisciplinary collaboration, health-centered medical service provision, and successful cases. Barriers include system complexity, security threats, lack of strategic planning and resource allocation, relevant personnel shortages, and inadequate investment. This study proposes 4 promotional strategies: encouraging more hospitals to migrate, enhancing hospitals' capabilities for migration, establishing a provincial-level unified medical hybrid multi-cloud platform, strengthening legal frameworks, and providing robust technical support. Conclusions: Cloud computing is an innovative technology that has gained significant attention from both the Chinese government and the global community. In order to effectively support the rapid growth of a novel, health-centered medical industry, it is imperative for Chinese health authorities and hospitals to seize this opportunity by implementing comprehensive strategies aimed at encouraging hospitals to migrate their HISs to the cloud. ", doi="10.2196/52080", url="https://medinform.jmir.org/2024/1/e52080", url="http://www.ncbi.nlm.nih.gov/pubmed/38315519" } @Article{info:doi/10.2196/53516, author="Koonce, Y. Taneya and Giuse, A. Dario and Williams, M. Annette and Blasingame, N. Mallory and Krump, A. Poppy and Su, Jing and Giuse, B. Nunzia", title="Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition", journal="JMIR Med Inform", year="2024", month="Jan", day="30", volume="12", pages="e53516", keywords="natural language processing", keywords="electronic health records", keywords="machine learning", keywords="data mining", keywords="knowledge management", keywords="NLP", doi="10.2196/53516", url="https://medinform.jmir.org/2024/1/e53516", url="http://www.ncbi.nlm.nih.gov/pubmed/38289670" } @Article{info:doi/10.2196/53112, author="Morris, Seymour James", title="A Call to Reconsider a Nationwide Electronic Health Record System: Correcting the Failures of the National Program for IT", journal="JMIR Med Inform", year="2023", month="Dec", day="28", volume="11", pages="e53112", keywords="electronic health record", keywords="EHR", keywords="medical record linkage", keywords="health information interoperability", keywords="health information management", keywords="health information systems", keywords="information systems", keywords="interoperability", keywords="health records", keywords="medical records", keywords="national", doi="10.2196/53112", url="https://medinform.jmir.org/2023/1/e53112" } @Article{info:doi/10.2196/49301, author="Schwab, D. Julian and Werle, D. Silke and H{\"u}hne, Rolf and Spohn, Hannah and Kaisers, X. Udo and Kestler, A. Hans", title="The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices", journal="JMIR Med Inform", year="2023", month="Dec", day="22", volume="11", pages="e49301", keywords="semantic terminology", keywords="semantic", keywords="terminology", keywords="terminologies", keywords="data linkage", keywords="interoperability", keywords="data exchange", keywords="SNOMED CT", keywords="LOINC", keywords="eHealth", keywords="patient-reported outcome questionnaires", keywords="requirement for standards", keywords="standard", keywords="standards", keywords="PRO", keywords="PROM", keywords="patient reported", doi="10.2196/49301", url="https://medinform.jmir.org/2023/1/e49301", url="http://www.ncbi.nlm.nih.gov/pubmed/38133917" } @Article{info:doi/10.2196/44265, author="Nourse, Rebecca and Dingler, Tilman and Kelly, Jaimon and Kwasnicka, Dominika and Maddison, Ralph", title="The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions", journal="J Med Internet Res", year="2023", month="Dec", day="18", volume="25", pages="e44265", keywords="smart home", keywords="health", keywords="chronic condition", keywords="chronic illness", keywords="digital health", keywords="technology", keywords="behavior change", keywords="wearable", keywords="smart technology", keywords="smart health", keywords="economic", keywords="cost", keywords="security", keywords="data storage", keywords="implementation", doi="10.2196/44265", url="https://www.jmir.org/2023/1/e44265", url="http://www.ncbi.nlm.nih.gov/pubmed/38109188" } @Article{info:doi/10.2196/44171, author="Zhu, Hongjian and Wong, Kee Weng", title="An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications", journal="J Med Internet Res", year="2023", month="Oct", day="16", volume="25", pages="e44171", keywords="doubly adaptive biased coin designs", keywords="model-based optimal designs", keywords="particle swarm optimization", keywords="repair mechanism", doi="10.2196/44171", url="https://www.jmir.org/2023/1/e44171", url="http://www.ncbi.nlm.nih.gov/pubmed/37843888" } @Article{info:doi/10.2196/47884, author="Herington, Jonathan and Connelly, Kay and Illes, Judy", title="Ethical Imperatives for Working With Diverse Populations in Digital Research", journal="J Med Internet Res", year="2023", month="Sep", day="18", volume="25", pages="e47884", keywords="digital health research", keywords="justice", keywords="research ethics", keywords="diversity", keywords="engagement", keywords="research participants", keywords="participatory", doi="10.2196/47884", url="https://www.jmir.org/2023/1/e47884", url="http://www.ncbi.nlm.nih.gov/pubmed/37721792" } @Article{info:doi/10.2196/51776, author="Fear, Kathleen and Gleber, Conrad", title="Shaping the Future of Older Adult Care: ChatGPT, Advanced AI, and the Transformation of Clinical Practice", journal="JMIR Aging", year="2023", month="Sep", day="13", volume="6", pages="e51776", keywords="generative AI", keywords="artificial intelligence", keywords="large language models", keywords="ChatGPT", keywords="Generative Pre-trained Transformer", doi="10.2196/51776", url="https://aging.jmir.org/2023/1/e51776", url="http://www.ncbi.nlm.nih.gov/pubmed/37703085" } @Article{info:doi/10.2196/47540, author="Tajabadi, Mohammad and Grabenhenrich, Linus and Ribeiro, Ad{\`e}le and Leyer, Michael and Heider, Dominik", title="Sharing Data With Shared Benefits: Artificial Intelligence Perspective", journal="J Med Internet Res", year="2023", month="Aug", day="29", volume="25", pages="e47540", keywords="federated learning", keywords="machine learning", keywords="medical data", keywords="fairness", keywords="data sharing", keywords="artificial intelligence", keywords="development", keywords="artificial intelligence model", keywords="applications", keywords="data analysis", keywords="diagnostic tool", keywords="tool", doi="10.2196/47540", url="https://www.jmir.org/2023/1/e47540", url="http://www.ncbi.nlm.nih.gov/pubmed/37642995" } @Article{info:doi/10.2196/40031, author="Chenais, Gabrielle and Lagarde, Emmanuel and Gil-Jardin{\'e}, C{\'e}dric", title="Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges", journal="J Med Internet Res", year="2023", month="May", day="23", volume="25", pages="e40031", keywords="viewpoint", keywords="ethics", keywords="artificial intelligence", keywords="emergency medicine", keywords="perspectives", keywords="mobile phone", doi="10.2196/40031", url="https://www.jmir.org/2023/1/e40031", url="http://www.ncbi.nlm.nih.gov/pubmed/36972306" } @Article{info:doi/10.2196/43871, author="Benis, Arriel and Haghi, Mostafa and Deserno, M. Thomas and Tamburis, Oscar", title="One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case", journal="JMIR Med Inform", year="2023", month="May", day="19", volume="11", pages="e43871", keywords="One Health", keywords="Digital Health", keywords="One Digital Health", keywords="accident and emergency informatics", keywords="eHealth", keywords="informatics", keywords="medicine", keywords="veterinary medicine", keywords="environmental monitoring", keywords="education", keywords="patient engagement", keywords="citizen science", keywords="data science", keywords="pets", keywords="human-animal bond", keywords="intervention", keywords="ambulatory monitoring", keywords="health monitoring", keywords="Internet of Things", keywords="smart environment", keywords="mobile phone", doi="10.2196/43871", url="https://medinform.jmir.org/2023/1/e43871", url="http://www.ncbi.nlm.nih.gov/pubmed/36305540" } @Article{info:doi/10.2196/44784, author="Harahap, Clydea Nabila and Handayani, Wuri Putu and Hidayanto, Nizar Achmad", title="Integrated Personal Health Record in Indonesia: Design Science Research Study", journal="JMIR Med Inform", year="2023", month="Mar", day="14", volume="11", pages="e44784", keywords="personal health record", keywords="integrated", keywords="Indonesia", keywords="design science", keywords="mobile phone", abstract="Background: Personal health records (PHRs) are consumer-centric tools designed to facilitate the tracking, management, and sharing of personal health information. PHR research has mainly been conducted in high-income countries rather than in low- and middle-income countries. Moreover, previous studies that proposed PHR design in low- and middle-income countries did not describe integration with other systems, or there was no stakeholder involvement in exploring PHR requirements. Objective: This study developed an integrated PHR architecture and prototype in Indonesia using design science research. We conducted the research in Indonesia, a low- to middle-income country with the largest population in Southeast Asia and a tiered health system. Methods: This study followed the design science research guidelines. The requirements were identified through interviews with 37 respondents from health organizations and a questionnaire with 1012 patients. Afterward, the proposed architecture and prototype were evaluated via interviews with 6 IT or eHealth experts. Results: The architecture design refers to The Open Group Architecture Framework version 9.2 and comprises 5 components: architecture vision, business architecture, application architecture, data architecture, and technology architecture. We developed a high-fidelity prototype for patients and physicians. In the evaluation, improvements were made to add the stakeholders and the required functionality to the PHR and add the necessary information to the functions that were developed in the prototype. Conclusions: We used design science to illustrate PHR integration in Indonesia, which involves related stakeholders in requirement gathering and evaluation. We developed architecture and application prototypes based on health systems in Indonesia, which comprise routine health services, including disease treatment and health examinations, as well as promotive and preventive health efforts. ", doi="10.2196/44784", url="https://medinform.jmir.org/2023/1/e44784", url="http://www.ncbi.nlm.nih.gov/pubmed/36917168" } @Article{info:doi/10.2196/41212, author="Li, Dongliang and Zhang, Rujia and Chen, Chun and Huang, Yunyun and Wang, Xiaoyi and Yang, Qingren and Zhu, Xuebo and Zhang, Xiangyang and Hao, Mo and Shui, Liming", title="Developing a Capsule Clinic---A 24-Hour Institution for Improving Primary Health Care Accessibility: Evidence From China", journal="JMIR Med Inform", year="2023", month="Jan", day="9", volume="11", pages="e41212", keywords="primary health care", keywords="accessibility", keywords="capsule clinic", keywords="24-hour clinic", keywords="big-data", keywords="China", keywords="United Nations", keywords="internet clinic", doi="10.2196/41212", url="https://medinform.jmir.org/2023/1/e41212", url="http://www.ncbi.nlm.nih.gov/pubmed/36622737" } @Article{info:doi/10.2196/40039, author="Alexander, Natasha and Aftandilian, Catherine and Guo, Lawrence Lin and Plenert, Erin and Posada, Jose and Fries, Jason and Fleming, Scott and Johnson, Alistair and Shah, Nigam and Sung, Lillian", title="Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study", journal="JMIR Med Inform", year="2022", month="Nov", day="17", volume="10", number="11", pages="e40039", keywords="machine learning", keywords="clinical utilization", keywords="preferences", keywords="qualitative interviews", abstract="Background: Given the costs of machine learning implementation, a systematic approach to prioritizing which models to implement into clinical practice may be valuable. Objective: The primary objective was to determine the health care attributes respondents at 2 pediatric institutions rate as important when prioritizing machine learning model implementation. The secondary objective was to describe their perspectives on implementation using a qualitative approach. Methods: In this mixed methods study, we distributed a survey to health system leaders, physicians, and data scientists at 2 pediatric institutions. We asked respondents to rank the following 5 attributes in terms of implementation usefulness: the clinical problem was common, the clinical problem caused substantial morbidity and mortality, risk stratification led to different actions that could reasonably improve patient outcomes, reducing physician workload, and saving money. Important attributes were those ranked as first or second most important. Individual qualitative interviews were conducted with a subsample of respondents. Results: Among 613 eligible respondents, 275 (44.9\%) responded. Qualitative interviews were conducted with 17 respondents. The most common important attributes were risk stratification leading to different actions (205/275, 74.5\%) and clinical problem causing substantial morbidity or mortality (177/275, 64.4\%). The attributes considered least important were reducing physician workload and saving money. Qualitative interviews consistently prioritized implementations that improved patient outcomes. Conclusions: Respondents prioritized machine learning model implementation where risk stratification would lead to different actions and clinical problems that caused substantial morbidity and mortality. Implementations that improved patient outcomes were prioritized. These results can help provide a framework for machine learning model implementation. ", doi="10.2196/40039", url="https://medinform.jmir.org/2022/11/e40039", url="http://www.ncbi.nlm.nih.gov/pubmed/36394938" } @Article{info:doi/10.2196/35138, author="Qin, Jiaxin and Wu, Jian", title="Realizing the Potential of Computer-Assisted Surgery by Embedding Digital Twin Technology", journal="JMIR Med Inform", year="2022", month="Nov", day="8", volume="10", number="11", pages="e35138", keywords="computer-assisted surgery", keywords="digital twin", keywords="virtual space", keywords="surgical navigation", keywords="remote surgery", doi="10.2196/35138", url="https://medinform.jmir.org/2022/11/e35138", url="http://www.ncbi.nlm.nih.gov/pubmed/36346669" } @Article{info:doi/10.2196/38557, author="Maletzky, Alexander and B{\"o}ck, Carl and Tschoellitsch, Thomas and Roland, Theresa and Ludwig, Helga and Thumfart, Stefan and Giretzlehner, Michael and Hochreiter, Sepp and Meier, Jens", title="Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities", journal="JMIR Med Inform", year="2022", month="Oct", day="21", volume="10", number="10", pages="e38557", keywords="electronic health record", keywords="medical data preparation", keywords="machine learning", keywords="retrospective data analysis", doi="10.2196/38557", url="https://medinform.jmir.org/2022/10/e38557", url="http://www.ncbi.nlm.nih.gov/pubmed/36269654" } @Article{info:doi/10.2196/39746, author="Wells, J. Brian and Downs, M. Stephen and Ostasiewski, Brian", title="Using Electronic Health Records for the Learning Health System: Creation of a Diabetes Research Registry", journal="JMIR Med Inform", year="2022", month="Sep", day="23", volume="10", number="9", pages="e39746", keywords="electronic health record", keywords="EHR", keywords="Learning Health System", keywords="registry", keywords="diabetes", doi="10.2196/39746", url="https://medinform.jmir.org/2022/9/e39746", url="http://www.ncbi.nlm.nih.gov/pubmed/36149742" } @Article{info:doi/10.2196/34304, author="Hu, Zoe and Hu, Ricky and Yau, Olivia and Teng, Minnie and Wang, Patrick and Hu, Grace and Singla, Rohit", title="Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint", journal="JMIR Med Inform", year="2022", month="Aug", day="15", volume="10", number="8", pages="e34304", keywords="medical education", keywords="artificial intelligence", keywords="health care trainees", keywords="AI", keywords="health care workers", doi="10.2196/34304", url="https://medinform.jmir.org/2022/8/e34304", url="http://www.ncbi.nlm.nih.gov/pubmed/35969464" } @Article{info:doi/10.2196/37756, author="Krzyzanowski, Brittany and Manson, M. Steven", title="Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward", journal="JMIR Med Inform", year="2022", month="Aug", day="3", volume="10", number="8", pages="e37756", keywords="Health Insurance Portability and Accountability Act", keywords="HIPAA", keywords="data privacy", keywords="health", keywords="maps", keywords="safe harbor", keywords="visualization", keywords="patient privacy", doi="10.2196/37756", url="https://medinform.jmir.org/2022/8/e37756", url="http://www.ncbi.nlm.nih.gov/pubmed/35921140" } @Article{info:doi/10.2196/39145, author="Pawelek, Jeff and Baca-Motes, Katie and Pandit, A. Jay and Berk, B. Benjamin and Ramos, Edward", title="The Power of Patient Engagement With Electronic Health Records as Research Participants", journal="JMIR Med Inform", year="2022", month="Jul", day="8", volume="10", number="7", pages="e39145", keywords="electronic health record", keywords="EHR", keywords="digital health technology", keywords="digital clinical trial", keywords="underrepresentation", keywords="underrepresented in biomedical research", keywords="biomedical research", doi="10.2196/39145", url="https://medinform.jmir.org/2022/7/e39145", url="http://www.ncbi.nlm.nih.gov/pubmed/35802410" } @Article{info:doi/10.2196/34204, author="Klimek, Peter and Baltic, Dejan and Brunner, Martin and Degelsegger-Marquez, Alexander and Garh{\"o}fer, Gerhard and Gouya-Lechner, Ghazaleh and Herzog, Arnold and Jilma, Bernd and K{\"a}hler, Stefan and Mikl, Veronika and Mraz, Bernhard and Ostermann, Herwig and R{\"o}hl, Claas and Scharinger, Robert and Stamm, Tanja and Strassnig, Michael and Wirthumer-Hoche, Christa and Pleiner-Duxneuner, Johannes", title="Quality Criteria for Real-world Data in Pharmaceutical Research and Health Care Decision-making: Austrian Expert Consensus", journal="JMIR Med Inform", year="2022", month="Jun", day="17", volume="10", number="6", pages="e34204", keywords="real-world data", keywords="real-world evidence", keywords="data quality", keywords="data quality criteria", keywords="RWD quality recommendations", keywords="pharmaceutical research", keywords="health care decision-making", keywords="quality criteria for RWD in health care", keywords="Gesellschaft f{\"u}r Pharmazeutische Medizin", keywords="GPMed", doi="10.2196/34204", url="https://medinform.jmir.org/2022/6/e34204", url="http://www.ncbi.nlm.nih.gov/pubmed/35713954" } @Article{info:doi/10.2196/32245, author="Zirikly, Ayah and Desmet, Bart and Newman-Griffis, Denis and Marfeo, E. Elizabeth and McDonough, Christine and Goldman, Howard and Chan, Leighton", title="Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case", journal="JMIR Med Inform", year="2022", month="Mar", day="18", volume="10", number="3", pages="e32245", keywords="natural language processing", keywords="text mining", keywords="bioinformatics", keywords="health informatics", keywords="machine learning", keywords="disability", keywords="mental health", keywords="functioning", keywords="NLP", keywords="electronic health record", keywords="framework", keywords="EHR", keywords="automation", keywords="eHealth", keywords="decision support", keywords="functional status", keywords="whole-person function", doi="10.2196/32245", url="https://medinform.jmir.org/2022/3/e32245", url="http://www.ncbi.nlm.nih.gov/pubmed/35302510" } @Article{info:doi/10.2196/27691, author="Liaw, R. Winston and Westfall, M. John and Williamson, S. Tyler and Jabbarpour, Yalda and Bazemore, Andrew", title="Primary Care: The Actual Intelligence Required for Artificial Intelligence to Advance Health Care and Improve Health", journal="JMIR Med Inform", year="2022", month="Mar", day="8", volume="10", number="3", pages="e27691", keywords="artificial intelligence", keywords="primary care", doi="10.2196/27691", url="https://medinform.jmir.org/2022/3/e27691", url="http://www.ncbi.nlm.nih.gov/pubmed/35258464" } @Article{info:doi/10.2196/33044, author="Luo, Gang", title="A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma", journal="JMIR Med Inform", year="2022", month="Mar", day="1", volume="10", number="3", pages="e33044", keywords="clinical decision support", keywords="forecasting", keywords="machine learning", keywords="patient care management", keywords="medical informatics", keywords="asthma", keywords="health care", keywords="health care systems", keywords="health care costs", keywords="prediction models", keywords="risk prediction", doi="10.2196/33044", url="https://medinform.jmir.org/2022/3/e33044", url="http://www.ncbi.nlm.nih.gov/pubmed/35230246" } @Article{info:doi/10.2196/33848, author="Ashworth, Henry and Ebrahim, Senan and Ebrahim, Hassaan and Bhaiwala, Zahra and Chilazi, Michael", title="A Free, Open-Source, Offline Digital Health System for Refugee Care", journal="JMIR Med Inform", year="2022", month="Feb", day="11", volume="10", number="2", pages="e33848", keywords="electronic health record", keywords="mHealth", keywords="refugee", keywords="displaced population", keywords="digital health", keywords="COVID-19", keywords="health care", abstract="Background: Rise of conflict, extreme weather events, and pandemics have led to larger displaced populations worldwide. Displaced populations have unique acute and chronic health needs that must be met by low-resource health systems. Electronic health records (EHRs) have been shown to improve health outcomes in displaced populations, but need to be adapted to meet the constraints of these health systems. Objective: The aim of this viewpoint is to describe the development and deployment of an EHR designed to care for displaced populations in low-resource settings. Methods: Using a human-centered design approach, we conducted in-depth interviews and focus groups with patients, health care providers, and administrators in Lebanon and Jordan to identify the essential EHR features. These features, including modular workflows, multilingual interfaces, and offline-first capabilities, led to the development of the Hikma Health EHR, which has been deployed in Lebanon and Nicaragua. Results: We report the successes and challenges from 12 months of Hikma Health EHR deployment in a mobile clinic providing care to Syrian refugees in Bekaa Valley, Lebanon. Successes include the EHR's ability to (1) increase clinical efficacy by providing detailed patient records, (2) be adaptable to the threats of COVID-19, and (3) improve organizational planning. Lessons learned include technical fixes to methods of identifying patients through name or their medical record ID. Conclusions: As the number of displaced people continues to rise globally, it is imperative that solutions are created to help maximize the health care they receive. Free, open-sourced, and adaptable EHRs can enable organizations to better provide for displaced populations. ", doi="10.2196/33848", url="https://medinform.jmir.org/2022/2/e33848", url="http://www.ncbi.nlm.nih.gov/pubmed/35147509" } @Article{info:doi/10.2196/32875, author="Sezgin, Emre and Sirrianni, Joseph and Linwood, L. Simon", title="Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model", journal="JMIR Med Inform", year="2022", month="Feb", day="10", volume="10", number="2", pages="e32875", keywords="natural language processing", keywords="artificial intelligence", keywords="generative pretrained transformer", keywords="clinical informatics", keywords="chatbot", doi="10.2196/32875", url="https://medinform.jmir.org/2022/2/e32875", url="http://www.ncbi.nlm.nih.gov/pubmed/35142635" } @Article{info:doi/10.2196/34038, author="Carolan, Elizabeth Jane and McGonigle, John and Dennis, Andrea and Lorgelly, Paula and Banerjee, Amitava", title="Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device", journal="JMIR Med Inform", year="2022", month="Jan", day="27", volume="10", number="1", pages="e34038", keywords="Artificial intelligence", keywords="machine learning", keywords="algorithm", keywords="software", keywords="risk assessment", keywords="informatics", doi="10.2196/34038", url="https://medinform.jmir.org/2022/1/e34038", url="http://www.ncbi.nlm.nih.gov/pubmed/35084352" } @Article{info:doi/10.2196/31837, author="Zhang, Xinyue and Saltman, Richard", title="Impact of Electronic Health Record Interoperability on Telehealth Service Outcomes", journal="JMIR Med Inform", year="2022", month="Jan", day="11", volume="10", number="1", pages="e31837", keywords="Electronic Health Records", keywords="Telehealth", keywords="Telemental health", keywords="Pandemic", keywords="Health outcomes", keywords="Health Policy", doi="10.2196/31837", url="https://medinform.jmir.org/2022/1/e31837", url="http://www.ncbi.nlm.nih.gov/pubmed/34890347" } @Article{info:doi/10.2196/25328, author="Madalinski, Mariusz and Prudham, Roger", title="Can Real-time Computer-Aided Detection Systems Diminish the Risk of Postcolonoscopy Colorectal Cancer?", journal="JMIR Med Inform", year="2021", month="Dec", day="24", volume="9", number="12", pages="e25328", keywords="artificial intelligence", keywords="colonoscopy", keywords="adenoma", keywords="real-time computer-aided detection", keywords="colonic polyp", doi="10.2196/25328", url="https://medinform.jmir.org/2021/12/e25328", url="http://www.ncbi.nlm.nih.gov/pubmed/34571490" } @Article{info:doi/10.2196/31527, author="Burmann, Anja and Tischler, Max and Fa{\ss}bach, Mira and Schneitler, Sophie and Meister, Sven", title="The Role of Physicians in Digitalizing Health Care Provision: Web-Based Survey Study", journal="JMIR Med Inform", year="2021", month="Nov", day="11", volume="9", number="11", pages="e31527", keywords="digitalization", keywords="digital transformation", keywords="health care", keywords="human factor", keywords="physicians", keywords="digital natives", keywords="web-based survey", keywords="digital health", abstract="Background: Digitalization affects all areas of society, including the health care sector. However, the digitalization of health care provision is progressing slowly compared to other sectors. In the professional and political literature, physicians are partially portrayed as digitalization sceptics. Thus, the role of physicians in this process requires further investigation. The theory of ``digital natives'' suggests a lower hurdle for younger generations to engage with digital technologies. Objective: The objective of this study was to investigate the role of physicians in the process of digitalizing health care provision in Germany and to assess the age factor. Methods: We conducted a large-scale study to assess the role of this professional group in the progress of the digital transformation of the German health care sector. Therefore, in an anonymous online survey, we inquired about the current digital penetration of the personal working environment, expectations, attitude toward, and concerns regarding digitalization. Based on these data, we studied associations with the nominal variable age and variations across 2 age groups. Results: The 1274 participants included in the study generally showed a high affinity towards digitalization with a mean of 3.88 on a 5-point Likert scale; 723 respondents (56.75\%) stated they personally use mobile apps in their everyday working life, with a weak tendency to be associated with the respondents' age ($\eta$=0.26). Participants saw the most noticeable existing benefits through digitalization in data quality and readability (882/1274, 69.23\%) and the least in patient engagement (213/1274, 16.72\%). Medical practitioners preponderantly expect further improvements through increased digitalization across almost all queried areas but the most in access to medical knowledge (1136/1274, 89.17\%), treatment of orphan diseases (1016/1274, 79.75\%), and medical research (1023/1274, 80.30\%). Conclusions: Respondents defined their role in the digitalization of health care provision as ambivalent: ``scrutinizing'' on the one hand but ``active'' and ``open'' on the other. A gap between willingness to participate and digital sovereignty was indicated. Thus, education on digitalization as a means to support health care provision should not only be included in the course of study but also in the continuing process of further and advanced training. ", doi="10.2196/31527", url="https://medinform.jmir.org/2021/11/e31527", url="http://www.ncbi.nlm.nih.gov/pubmed/34545813" } @Article{info:doi/10.2196/20046, author="Busse, Sophie Theresa and Kernebeck, Sven and Nef, Larissa and Rebacz, Patrick and Kickbusch, Ilona and Ehlers, Peter Jan", title="Views on Using Social Robots in Professional Caregiving: Content Analysis of a Scenario Method Workshop", journal="J Med Internet Res", year="2021", month="Nov", day="10", volume="23", number="11", pages="e20046", keywords="social robots", keywords="robotics", keywords="health care sector", keywords="health personnel", keywords="ethics", keywords="forecasting", keywords="trends", keywords="technology", keywords="digital transformation", keywords="professional caregiving", abstract="Background: Interest in digital technologies in the health care sector is growing and can be a way to reduce the burden on professional caregivers while helping people to become more independent. Social robots are regarded as a special form of technology that can be usefully applied in professional caregiving with the potential to focus on interpersonal contact. While implementation is progressing slowly, a debate on the concepts and applications of social robots in future care is necessary. Objective: In addition to existing studies with a focus on societal attitudes toward social robots, there is a need to understand the views of professional caregivers and patients. This study used desired future scenarios to collate the perspectives of experts and analyze the significance for developing the place of social robots in care. Methods: In February 2020, an expert workshop was held with 88 participants (health professionals and educators; [PhD] students of medicine, health care, professional care, and technology; patient advocates; software developers; government representatives; and research fellows) from Austria, Germany, and Switzerland. Using the scenario methodology, the possibilities of analog professional care (Analog Care), fully robotic professional care (Robotic Care), teams of robots and professional caregivers (Deep Care), and professional caregivers supported by robots (Smart Care) were discussed. The scenarios were used as a stimulus for the development of ideas about future professional caregiving. The discussion was evaluated using qualitative content analysis. Results: The majority of the experts were in favor of care in which people are supported by technology (Deep Care) and developed similar scenarios with a focus on dignity-centeredness. The discussions then focused on the steps necessary for its implementation, highlighting a strong need for the development of eHealth competence in society, a change in the training of professional caregivers, and cross-sectoral concepts. The experts also saw user acceptance as crucial to the use of robotics. This involves the acceptance of both professional caregivers and care recipients. Conclusions: The literature review and subsequent workshop revealed how decision-making about the value of social robots depends on personal characteristics related to experience and values. There is therefore a strong need to recognize individual perspectives of care before social robots become an integrated part of care in the future. ", doi="10.2196/20046", url="https://www.jmir.org/2021/11/e20046", url="http://www.ncbi.nlm.nih.gov/pubmed/34757318" } @Article{info:doi/10.2196/30605, author="Drazich, F. Brittany and Nyikadzino, Yeukai and Gleason, T. Kelly", title="A Program to Improve Digital Access and Literacy Among Community Stakeholders: Cohort Study", journal="JMIR Form Res", year="2021", month="Nov", day="10", volume="5", number="11", pages="e30605", keywords="technology", keywords="disparities", keywords="digital access", keywords="digital literacy", keywords="community", keywords="stakeholders", keywords="digital health", keywords="digital divide", keywords="patient-centered outcomes", abstract="Background: For many research teams, the role of community stakeholders is critical. However, community stakeholders, especially those in low-income settings, are at risk of being excluded from research and community engagement initiatives during and after the COVID-19 pandemic because of the rapid transition to digital operations. Objective: We aimed to describe the implementation and feasibility of a program called Addressing the Digital Divide to Improve Patient-Centered Outcomes Research, which was designed to address barriers to technology use, and to examine changes in participants' perceived comfort with digital technology before and after the program. Methods: To promote full engagement, we worked with 20 existing community leaders to cocreate a training course on using digital technology. We assessed the frequency of technology use and comfort with technology through an adapted 8-item version of the Functional Assessment of Comfort Employing Technology Scale and used the Wilcoxon signed-rank test for survey analysis. We also conducted a focus group session with 10 participants and then performed reflective journaling and content analysis to determine emergent themes. Results: We found that the program was feasible to implement and worthwhile for participants (15/16, 94\%). After the program, the participants perceived an increase in the frequency of technology use (z=2.76, P=.006). The participants reported that the program was successful because of the technology training program, but recommended that the program have a slower pace and include a helpline number that they could call with questions. Conclusions: Future programs should consider that populations with low literacy view technology training as a core element to decreasing technology disparity. This study demonstrates that through low-cost input, community members can be provided the resources and training needed to virtually participate in research studies or community engagement initiatives. ", doi="10.2196/30605", url="https://formative.jmir.org/2021/11/e30605", url="http://www.ncbi.nlm.nih.gov/pubmed/34757316" } @Article{info:doi/10.2196/30873, author="Lin, Yuchen and Lemos, Martin and Neuschaefer-Rube, Christiane", title="Digital Health and Digital Learning Experiences Across Speech-Language Pathology, Phoniatrics, and Otolaryngology: Interdisciplinary Survey Study", journal="JMIR Med Educ", year="2021", month="Nov", day="5", volume="7", number="4", pages="e30873", keywords="digital learning", keywords="e-learning", keywords="speech-language pathology", keywords="phoniatrics", keywords="otolaryngology", keywords="communication disorders", keywords="mobile phone", abstract="Background: Advances in digital health and digital learning are transforming the lives of patients, health care providers, and health professional students. In the interdisciplinary field of communication sciences and disorders (CSD), digital uptake and incorporation of digital topics and technologies into clinical training programs has lagged behind other medical fields. There is a need to understand professional and student experiences, opinions, and needs regarding digital health and learning topics so that effective strategies for implementation can be optimized. Objective: This cross-sectional survey study aims to interdisciplinarily investigate professional and student knowledge, use, attitudes, and preferences toward digital health and learning in the German-speaking population. Methods: An open-ended, web-based survey was developed and conducted with professionals and students in CSD including phoniatricians and otolaryngologists, speech-language pathologists (German: Logop{\"a}d*innen), medical students, and speech-language pathology students. Differences in knowledge, use, attitudes, and preferences across profession, generation, and years of experience were analyzed. Results: A total of 170 participants completed the survey. Respondents demonstrated greater familiarity with digital learning as opposed to eHealth concepts. Significant differences were noted across profession (P<.001), generation (P=.001), and years of experience (P<.001), which demonstrated that students and younger participants were less familiar with digital health terminology. Professional (P<.001) and generational differences were also found (P=.04) in knowledge of digital therapy tools, though no significant differences were found for digital learning tools. Participants primarily used computers, tablets, and mobile phones; non--eHealth-specific tools (eg, word processing and videoconferencing applications); and digital formats such as videos, web courses, and apps. Many indicated a desire for more interactive platforms, such as virtual reality. Significant differences were found across generations for positive views toward digitalization (P<.001) and across profession for feelings of preparedness (P=.04). Interestingly, across profession (P=.03), generation (P=.006), and years of experience (P=.01), students and younger participants demonstrated greater support for medical certification. Commonly reported areas of concern included technical difficulties, quality and validity of digital materials, data privacy, and social presence. Respondents tended to prefer blended learning, a limited to moderate level of interactivity, and time and space--flexible learning environments (63/170, 37.1\%), with a notable proportion still preferring traditional time and space--dependent learning (49/170, 28.8\%). Conclusions: This comprehensive investigation into the current state of CSD student and professional opinions and experiences has shown that incorporation of digital topics and skills into academic and professional development curricula will be crucial for ensuring that the field is prepared for the ever-digitalizing health care environment. Deeper empirical investigation into efficacy and acceptance of digital learning and practice strategies and systematic training and practical organizational supports must be planned to ensure adaptive education and practice. ", doi="10.2196/30873", url="https://mededu.jmir.org/2021/4/e30873", url="http://www.ncbi.nlm.nih.gov/pubmed/34738911" } @Article{info:doi/10.2196/30768, author="Nelligan, K. Rachel and Hinman, S. Rana and McManus, Fiona and Lamb, E. Karen and Bennell, L. Kim", title="Moderators of the Effect of a Self-directed Digitally Delivered Exercise Program for People With Knee Osteoarthritis: Exploratory Analysis of a Randomized Controlled Trial", journal="J Med Internet Res", year="2021", month="Oct", day="29", volume="23", number="10", pages="e30768", keywords="digital", keywords="text messaging", keywords="exercise", keywords="moderators", keywords="osteoarthritis", keywords="RCT", keywords="clinical trial", keywords="subgroups", keywords="pain", keywords="function", keywords="knee osteoarthritis", keywords="rehabilitation", keywords="digital health", abstract="Background: A 24-week self-directed digitally delivered intervention was found to improve pain and function in people with knee osteoarthritis (OA). However, it is possible that this intervention may be better suited to certain subgroups of people with knee OA compared to others. Objective: The aim of this study was to explore whether certain individual baseline characteristics moderate the effects of a self-directed digitally delivered intervention on changes in pain and function over 24 weeks in people with knee OA. Methods: An exploratory analysis was conducted on data from a randomized controlled trial involving 206 people with a clinical diagnosis of knee OA. This trial compared a self-directed digitally delivered intervention comprising of web-based education, exercise, and physical activity program supported by automated exercise behavior change mobile phone text messages to web-based education alone (control). The primary outcomes were changes in overall knee pain (assessed on an 11-point numerical rating scale) and physical function (assessed using the Western Ontario and McMaster Universities Osteoarthritis Index function subscale [WOMAC]) at 24 weeks. Five baseline patient characteristics were selected as the potential moderators: (1) number of comorbidities, (2) number of other painful joints, (3) pain self-efficacy, (4) exercise self-efficacy, and (5) self-perceived importance of exercise. Separate linear regression models for each primary outcome and each potential moderator were fit, including treatment group, moderator, and interaction between treatment group and moderator, adjusting for the outcome at baseline. Results: There was evidence that pain self-efficacy moderated the effect of the intervention on physical function compared to the control at 24 weeks (interaction P=.02). Posthoc assessment of the mean change in WOMAC function by treatment arm showed that each 1-unit increase in baseline pain self-efficacy was associated with a 1.52 (95\% CI 0.27 to 2.78) unit improvement in the control group. In contrast, a reduction of 0.62 (95\% CI --1.93 to 0.68) units was observed in the intervention group with each unit increase in pain self-efficacy. There was only weak evidence that pain self-efficacy moderated the effect of the intervention on pain and that number of comorbidities, number of other painful joints, exercise self-efficacy, or exercise importance moderated the effect of the intervention on pain or function. Conclusions: With the exception of pain self-efficacy, which moderated changes in function but not pain, we found limited evidence that our selected baseline patient characteristics moderated intervention outcomes. This indicates that people with a range of baseline characteristics respond similarly to the unsupervised digitally delivered exercise intervention. As these findings are exploratory in nature, they require confirmation in future studies. ", doi="10.2196/30768", url="https://www.jmir.org/2021/10/e30768", url="http://www.ncbi.nlm.nih.gov/pubmed/34714252" } @Article{info:doi/10.2196/18471, author="Sarker, Abeed and Al-Garadi, Ali Mohammed and Yang, Yuan-Chi and Choi, Jinho and Quyyumi, A. Arshed and Martin, S. Greg", title="Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts", journal="JMIR Med Inform", year="2021", month="Sep", day="28", volume="9", number="9", pages="e18471", keywords="natural language processing", keywords="text mining", keywords="patient-centered care", keywords="evidence-based medicine", keywords="medical informatics", doi="10.2196/18471", url="https://medinform.jmir.org/2021/9/e18471", url="http://www.ncbi.nlm.nih.gov/pubmed/34581670" } @Article{info:doi/10.2196/29374, author="Mohan, Vishnu and Garrison, Cort and Gold, A. Jeffrey", title="Using a New Model of Electronic Health Record Training to Reduce Physician Burnout: A Plan for Action", journal="JMIR Med Inform", year="2021", month="Sep", day="20", volume="9", number="9", pages="e29374", keywords="electronic health records", keywords="clinician burnout", keywords="EHR training", keywords="clinician wellness", keywords="after-hours EHR use", keywords="EHR", keywords="patient data", keywords="burnout", keywords="simulation", keywords="efficiency", keywords="optimization", keywords="well-being", doi="10.2196/29374", url="https://medinform.jmir.org/2021/9/e29374", url="http://www.ncbi.nlm.nih.gov/pubmed/34325400" } @Article{info:doi/10.2196/28776, author="Kulkarni, Viraj and Gawali, Manish and Kharat, Amit", title="Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice", journal="JMIR Med Inform", year="2021", month="Sep", day="9", volume="9", number="9", pages="e28776", keywords="artificial intelligence", keywords="AI", keywords="machine learning", keywords="deep learning", keywords="radiology", keywords="privacy", keywords="neural networks", keywords="deployment", doi="10.2196/28776", url="https://medinform.jmir.org/2021/9/e28776", url="http://www.ncbi.nlm.nih.gov/pubmed/34499049" } @Article{info:doi/10.2196/23219, author="McKillop, Mollie and Snowdon, Jane and Willis, C. Van and Alevy, Shira and Rizvi, Rubina and Rewalt, Karen and Lefebvre-Paill{\'e}, Charlyne and Kassler, William and Purcell Jackson, Gretchen", title="A System to Support Diverse Social Program Management", journal="JMIR Med Inform", year="2021", month="Aug", day="30", volume="9", number="8", pages="e23219", keywords="other clinical informatics applications", keywords="process management tools", keywords="requirements analysis and design", keywords="consumer health informatics", keywords="public health", abstract="Background: Social programs are services provided by governments, nonprofits, and other organizations to help improve the health and well-being of individuals, families, and communities. Social programs aim to deliver services effectively and efficiently, but they are challenged by information silos, limited resources, and the need to deliver frequently changing mandated benefits. Objective: We aim to explore how an information system designed for social programs helps deliver services effectively and efficiently across diverse programs. Methods: This viewpoint describes the configurable and modular architecture of Social Program Management (SPM), a system to support efficient and effective delivery of services through a wide range of social programs and lessons learned from implementing SPM across diverse settings. We explored usage data to inform the engagement and impact of SPM on the efficient and effective delivery of services. Results: The features and functionalities of SPM seem to support the goals of social programs. We found that SPM provides fundamental management processes and configurable program-specific components to support social program administration; has been used by more than 280,000 caseworkers serving more than 30 million people in 13 countries; contains features designed to meet specific user requirements; supports secure information sharing and collaboration through data standardization and aggregation; and offers configurability and flexibility, which are important for digital transformation and organizational change. Conclusions: SPM is a user-centered, configurable, and flexible system for managing social program workflows. ", doi="10.2196/23219", url="https://medinform.jmir.org/2021/8/e23219", url="http://www.ncbi.nlm.nih.gov/pubmed/34459741" } @Article{info:doi/10.2196/16293, author="Gonzales, Aldren and Smith, R. Scott and Dullabh, Prashila and Hovey, Lauren and Heaney-Huls, Krysta and Robichaud, Meagan and Boodoo, Roger", title="Potential Uses of Blockchain Technology for Outcomes Research on Opioids", journal="JMIR Med Inform", year="2021", month="Aug", day="27", volume="9", number="8", pages="e16293", keywords="blockchain", keywords="distributed ledger", keywords="opioid crisis", keywords="outcomes research", keywords="patient-centered outcomes research", keywords="mobile phone", doi="10.2196/16293", url="https://medinform.jmir.org/2021/8/e16293", url="http://www.ncbi.nlm.nih.gov/pubmed/34448721" } @Article{info:doi/10.2196/27449, author="Hogan, Katie and Macedo, Briana and Macha, Venkata and Barman, Arko and Jiang, Xiaoqian", title="Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation", journal="JMIR Med Inform", year="2021", month="Jul", day="19", volume="9", number="7", pages="e27449", keywords="contact tracing", keywords="COVID-19", keywords="privacy", keywords="smartphone apps", keywords="mobile phone apps", keywords="health information", keywords="electronic health", keywords="eHealth", keywords="pandemic", keywords="app", keywords="mobile health", keywords="mHealth", doi="10.2196/27449", url="https://medinform.jmir.org/2021/7/e27449", url="http://www.ncbi.nlm.nih.gov/pubmed/34254937" } @Article{info:doi/10.2196/28921, author="Makridis, Christos and Hurley, Seth and Klote, Mary and Alterovitz, Gil", title="Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans", journal="JMIR Med Inform", year="2021", month="Jun", day="2", volume="9", number="6", pages="e28921", keywords="artificial intelligence", keywords="ethics", keywords="veterans", keywords="health data", keywords="technology", keywords="Veterans Affairs", keywords="health technology", keywords="data", abstract="Background: Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on establishing principles for the trustworthy use of AI. Adhering to these principles is especially important for ensuring that the development and application of AI raises economic and social welfare, including among vulnerable groups and veterans. Objective: We explore the newly developed principles around trustworthy AI and how they can be readily applied at scale to vulnerable groups that are potentially less likely to benefit from technological advances. Methods: Using the US Department of Veterans Affairs as a case study, we explore the principles of trustworthy AI that are of particular interest for vulnerable groups and veterans. Results: We focus on three principles: (1) designing, developing, acquiring, and using AI so that the benefits of its use significantly outweigh the risks and the risks are assessed and managed; (2) ensuring that the application of AI occurs in well-defined domains and is accurate, effective, and fit for the intended purposes; and (3) ensuring that the operations and outcomes of AI applications are sufficiently interpretable and understandable by all subject matter experts, users, and others. Conclusions: These principles and applications apply more generally to vulnerable groups, and adherence to them can allow the VA and other organizations to continue modernizing their technology governance, leveraging the gains of AI while simultaneously managing its risks. ", doi="10.2196/28921", url="https://medinform.jmir.org/2021/6/e28921", url="http://www.ncbi.nlm.nih.gov/pubmed/34076584" } @Article{info:doi/10.2196/27778, author="Luo, Gang", title="A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support", journal="JMIR Med Inform", year="2021", month="May", day="27", volume="9", number="5", pages="e27778", keywords="clinical decision support", keywords="database management systems", keywords="forecasting", keywords="machine learning", keywords="electronic medical records", doi="10.2196/27778", url="https://medinform.jmir.org/2021/5/e27778", url="http://www.ncbi.nlm.nih.gov/pubmed/34042600" } @Article{info:doi/10.2196/21874, author="Ologeanu-Taddei, Roxana", title="Assessment of mHealth Interventions: Need for New Studies, Methods, and Guidelines for Study Designs", journal="JMIR Med Inform", year="2020", month="Nov", day="18", volume="8", number="11", pages="e21874", keywords="eHealth", keywords="mHealth", keywords="usability", keywords="management", keywords="survey", keywords="trust", keywords="guidelines", keywords="evaluation", doi="10.2196/21874", url="http://medinform.jmir.org/2020/11/e21874/", url="http://www.ncbi.nlm.nih.gov/pubmed/33206060" } @Article{info:doi/10.2196/20265, author="Kao, David and Larson, Cynthia and Fletcher, Dana and Stegner, Kris", title="Clinical Decision Support May Link Multiple Domains to Improve Patient Care: Viewpoint", journal="JMIR Med Inform", year="2020", month="Oct", day="16", volume="8", number="10", pages="e20265", keywords="clinical decision support", keywords="population medicine", keywords="evidence-based medicine", keywords="precision medicine", keywords="care management", keywords="electronic health records", doi="10.2196/20265", url="https://medinform.jmir.org/2020/10/e20265", url="http://www.ncbi.nlm.nih.gov/pubmed/33064106" } @Article{info:doi/10.2196/17429, author="Held, Philip and Boley, A. Randy and Faig, G. Walter and O'Toole, A. John and Desai, Imran and Zalta, K. Alyson and Khan, Jawad and Sims, Shannon and Brennan, B. Michael and Van Horn, Rebecca and Glover, C. Angela and Hota, N. Bala and Patty, D. Brian and Rab, Shafiq S. and Pollack, H. Mark and Karnik, S. Niranjan", title="The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records", journal="JMIR Form Res", year="2020", month="Apr", day="6", volume="4", number="4", pages="e17429", keywords="electronic health record", keywords="data collection", keywords="veterans", doi="10.2196/17429", url="https://formative.jmir.org/2020/4/e17429", url="http://www.ncbi.nlm.nih.gov/pubmed/32250276" } @Article{info:doi/10.2196/medinform.8207, author="Desveaux, Laura and Shaw, James and Wallace, Ross and Bhattacharyya, Onil and Bhatia, Sacha R. and Jamieson, Trevor", title="Examining Tensions That Affect the Evaluation of Technology in Health Care: Considerations for System Decision Makers From the Perspective of Industry and Evaluators", journal="JMIR Med Inform", year="2017", month="Dec", day="08", volume="5", number="4", pages="e50", keywords="technology", keywords="evaluation", keywords="policy", keywords="healthcare", doi="10.2196/medinform.8207", url="http://medinform.jmir.org/2017/4/e50/", url="http://www.ncbi.nlm.nih.gov/pubmed/29222075" } @Article{info:doi/10.2196/medinform.7476, author="Yen, Po-Yin and McAlearney, Scheck Ann and Sieck, J. Cynthia and Hefner, L. Jennifer and Huerta, R. Timothy", title="Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation", journal="JMIR Med Inform", year="2017", month="Sep", day="07", volume="5", number="3", pages="e28", keywords="health information technology", keywords="adaptation", keywords="adoption", keywords="acceptance", doi="10.2196/medinform.7476", url="http://medinform.jmir.org/2017/3/e28/", url="http://www.ncbi.nlm.nih.gov/pubmed/28882812" } @Article{info:doi/10.2196/medinform.7627, author="Deliberato, Oct{\'a}vio Rodrigo and Celi, Anthony Leo and Stone, J. David", title="Clinical Note Creation, Binning, and Artificial Intelligence", journal="JMIR Med Inform", year="2017", month="Aug", day="03", volume="5", number="3", pages="e24", keywords="electronic health records", keywords="artificial Intelligence", keywords="clinical informatics", doi="10.2196/medinform.7627", url="http://medinform.jmir.org/2017/3/e24/", url="http://www.ncbi.nlm.nih.gov/pubmed/28778845" } @Article{info:doi/10.2196/medinform.5571, author="Lea, Christopher Nathan and Nicholls, Jacqueline and Dobbs, Christine and Sethi, Nayha and Cunningham, James and Ainsworth, John and Heaven, Martin and Peacock, Trevor and Peacock, Anthony and Jones, Kerina and Laurie, Graeme and Kalra, Dipak", title="Data Safe Havens and Trust: Toward a Common Understanding of Trusted Research Platforms for Governing Secure and Ethical Health Research", journal="JMIR Med Inform", year="2016", month="Jun", day="21", volume="4", number="2", pages="e22", keywords="trusted research platforms", keywords="data safe havens", keywords="trusted researchers", keywords="legislative and regulatory compliance", keywords="public engagement", keywords="public involvement", keywords="clinical research support", keywords="health record linkage supported research", keywords="genomics research support", doi="10.2196/medinform.5571", url="http://medinform.jmir.org/2016/2/e22/", url="http://www.ncbi.nlm.nih.gov/pubmed/27329087" } @Article{info:doi/10.2196/ijmr.4217, author="Talboom-Kamp, PWA Esther and Verdijk, A. Noortje and Harmans, M. Lara and Numans, E. Mattijs and Chavannes, H. Niels", title="An eHealth Platform to Manage Chronic Disease in Primary Care: An Innovative Approach", journal="Interact J Med Res", year="2016", month="Feb", day="09", volume="5", number="1", pages="e5", keywords="eHealth", keywords="self-management", keywords="anticoagulation clinic", keywords="chronic obstructive pulmonary disease", keywords="venous thromboembolism", keywords="integrated disease management", keywords="chronically ill", keywords="telemonitoring", keywords="primary care", doi="10.2196/ijmr.4217", url="http://www.i-jmr.org/2016/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/26860333" } @Article{info:doi/10.2196/medinform.4192, author="Celi, Anthony Leo and Marshall, David Jeffrey and Lai, Yuan and Stone, J. David", title="Disrupting Electronic Health Records Systems: The Next Generation", journal="JMIR Med Inform", year="2015", month="Oct", day="23", volume="3", number="4", pages="e34", keywords="clinical decision making", keywords="clinical decision support", keywords="electronic health records", keywords="electronic notes", doi="10.2196/medinform.4192", url="http://medinform.jmir.org/2015/4/e34/", url="http://www.ncbi.nlm.nih.gov/pubmed/26500106" } @Article{info:doi/10.2196/medinform.4286, author="Waller, Amy and Forshaw, Kristy and Carey, Mariko and Robinson, Sancha and Kerridge, Ross and Proietto, Anthony and Sanson-Fisher, Rob", title="Optimizing Patient Preparation and Surgical Experience Using eHealth Technology", journal="JMIR Med Inform", year="2015", month="Sep", day="01", volume="3", number="3", pages="e29", keywords="eHealth", keywords="perioperative", keywords="postoperative", keywords="preoperative", keywords="surgery", doi="10.2196/medinform.4286", url="http://medinform.jmir.org/2015/3/e29/", url="http://www.ncbi.nlm.nih.gov/pubmed/26330206" } @Article{info:doi/10.2196/medinform.3205, author="Harvey, Harlan and Krishnaraj, Arun and Alkasab, K. Tarik", title="Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records", journal="JMIR Med Inform", year="2014", month="Mar", day="11", volume="2", number="1", pages="e4", keywords="medical informatics", keywords="medical records systems", keywords="computerized", keywords="health information management", doi="10.2196/medinform.3205", url="http://medinform.jmir.org/2014/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/25601018" }