%0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e53542 %T Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint %A Demuth,Stanislas %A De Sèze,Jérôme %A Edan,Gilles %A Ziemssen,Tjalf %A Simon,Françoise %A Gourraud,Pierre-Antoine %K digital twin %K artificial intelligence %K data architecture %K synthetic data %K computational modeling %K precision medicine %K conceptual clarification %K conceptual %K patient %K medicine %K health record %K digital records %K synthetic patient %D 2025 %7 28.1.2025 %9 %J JMIR Med Inform %G English %X Precision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical “digital twin” has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations. Here, we propose a medical digital twin framework with a data-centric approach. We argue that a single digital representation of patients cannot support all the data uses of digital twins for technical and regulatory reasons. Instead, we propose a data architecture leveraging three main families of digital representations: (1) multimodal dashboards integrating various raw health records at points of care to assist with perception and documentation, (2) virtual patients, which provide nonsensitive data for collective secondary uses, and (3) individual predictions that support clinical decisions. For a given patient, multiple digital representations may be generated according to the different clinical pathways the patient goes through, each tailored to balance the trade-offs associated with the respective intended uses. Therefore, our proposed framework conceives the medical digital twin as a data architecture leveraging several digital representations of patients along clinical pathways. %R 10.2196/53542 %U https://medinform.jmir.org/2025/1/e53542 %U https://doi.org/10.2196/53542 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e57385 %T Digital Health Innovations to Catalyze the Transition to Value-Based Health Care %A Zhang,Lan %A Bullen,Christopher %A Chen,Jinsong %K digital health %K value-based health care %K VBHC %K patient-reported outcome measures %K PROM %K digital transformation %K health care innovation %K patient-centric care %K health technology %K patient-reported outcome %K PRO %K outcome measure %K telehealth %K telemedicine %K eHealth %K personalized %K customized %K engagement %K patient-centered care %K standardization %K implementation %D 2025 %7 20.1.2025 %9 %J JMIR Med Inform %G English %X The health care industry is currently going through a transformation due to the integration of technologies and the shift toward value-based health care (VBHC). This article explores how digital health solutions play a role in advancing VBHC, highlighting both the challenges and opportunities associated with adopting these technologies. Digital health, which includes mobile health, wearable devices, telehealth, and personalized medicine, shows promise in improving diagnostic accuracy, treatment options, and overall health outcomes. The article delves into the concept of transformation in health care by emphasizing its potential to reform care delivery through data communication, patient engagement, and operational efficiency. Moreover, it examines the principles of VBHC, with a focus on patient outcomes, and emphasizes how digital platforms play a role in treatment among tertiary hospitals by using patient-reported outcome measures. The article discusses challenges that come with implementing VBHC, such as stakeholder engagement and standardization of patient-reported outcome measures. It also highlights the role played by health innovators in facilitating the transition toward VBHC models. Through real-life case examples, this article illustrates how digital platforms have had an impact on efficiencies, patient outcomes, and empowerment. In conclusion, it envisions directions for solutions in VBHC by emphasizing the need for interoperability, standardization, and collaborative efforts among stakeholders to fully realize the potential of digital transformation in health care. This research highlights the impact of digital health in creating a health care system that focuses on providing high-quality, efficient, and patient-centered care. %R 10.2196/57385 %U https://medinform.jmir.org/2025/1/e57385 %U https://doi.org/10.2196/57385 %0 Journal Article %@ 2561-6722 %I JMIR Publications %V 8 %N %P e55235 %T A Holistic Digital Health Framework to Support Health Prevention Strategies in the First 1000 Days %A Gabrielli,Silvia %A Mayora Ibarra,Oscar %A Forti,Stefano %K digital health %K digital therapeutics %K behavioral intervention technology %K prevention %K citizen science %K first 1000 days %D 2025 %7 16.1.2025 %9 %J JMIR Pediatr Parent %G English %X The first 1000 days of a child’s life, spanning from the time of conception until 2 years of age, are a key period of laying down the foundations of optimum health, growth, and development across the lifespan. Although the role of health prevention programs targeting families and children in the first 1000 days of life is well recognized, investments in this key period are scarce, and the provision of adequate health care services is insufficient. The aim of this viewpoint is to provide a holistic digital health framework cocreated with policy makers, health care professionals, and families to support more effective efforts and health care programs dedicated to the first 1000 days of life as the first line of prevention. The framework provides recommendations for leveraging on behavioral intervention technology and digital therapeutics solutions augmented by artificial intelligence to support the effective deployment of health prevention programs to families. The framework also encourages the adoption of a citizen science approach to co-design and evolve the digital health interventions with all relevant stakeholders in a real-world research perspective. %R 10.2196/55235 %U https://pediatrics.jmir.org/2025/1/e55235 %U https://doi.org/10.2196/55235 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e66109 %T Teaching in the Digital Age—Developing a Support Program for Nursing Education Providers: Design-Based Research %A Walzer,Stefan %A Barthel,Carolin %A Pazouki,Ronja %A Marx,Helga %A Ziegler,Sven %A Koenig,Peter %A Kugler,Christiane %A Jobst,Stefan %+ Care and Technology Lab, Furtwangen University, Robert-Gerwig-Platz 1, Furtwangen im Schwarzwald, 78120, Germany, 49 7723 920 2957, stefan.walzer@hs-furtwangen.de %K digital competencies %K nursing education %K support program %K needs assessment %K design-based research %K feasibility study %K nursing education provider %K qualitative research %K nurse %K health care %K focus group %K digital age %K expert consultation %K thematic content analysis %K feasibility test %K satisfaction %K competency-based approach %K workplace barrier %K health care digitalization %K digital technology %D 2025 %7 15.1.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Health care systems and the nursing profession worldwide are being transformed by technology and digitalization. Nurses acquire digital competence through their own experience in daily practice, but also from education and training; nursing education providers thus play an important role. While nursing education providers have some level of digital competence, there is a need for ongoing training and support for them to develop more advanced skills and effectively integrate technology into their teaching. Objective: This study aims to develop a needs-based support program for nursing education providers to foster digital competencies and to test this intervention. Methods: We used a design-based research approach, incorporating iterative development with expert consultation to create and evaluate a support program for nursing education providers. Focus groups were conducted online to assess needs, and thematic content analysis was used to derive key insights. The support program was then refined through expert feedback and subjected to a feasibility and satisfaction test, with participant evaluations analyzed descriptively. Results: Six main categories emerged from the focus groups, highlighting key areas, including the use of digital technology, ongoing support needs, and the current state of digitalization in nursing education. The support program was developed based on these findings, with expert validation leading to adjustments in timing, content prioritization, and platform integration. Preliminary testing showed good overall satisfaction with the support program, although participants suggested improvements in content relevance and digital platform usability. Conclusions: Although the feasibility test showed high satisfaction with the support program, low participation rates and limited perceived knowledge gain were major concerns. The results suggest that while the program was well received, further refinements, including a focus on competency-based approaches and addressing workplace barriers, are needed to increase participation and effectiveness of such interventions. The findings of this research can be used as a basis for the development of similar programs in other educational and health care contexts. %M 39813674 %R 10.2196/66109 %U https://formative.jmir.org/2025/1/e66109 %U https://doi.org/10.2196/66109 %U http://www.ncbi.nlm.nih.gov/pubmed/39813674 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59601 %T The CeHRes Roadmap 2.0: Update of a Holistic Framework for Development, Implementation, and Evaluation of eHealth Technologies %A Kip,Hanneke %A Beerlage-de Jong,Nienke %A van Gemert-Pijnen,Lisette J E W C %A Kelders,Saskia M %+ Section of Psychology, Health & Technology, Centre for eHealth and Wellbeing, University of Twente, Drienerlolaan 5, Enschede, 7522, Netherlands, 31 534896536, h.kip@utwente.nl %K eHealth development %K eHealth implementation %K CeHRes Roadmap %K participatory development %K human-centered design %K persuasive design %K eHealth framework %D 2025 %7 13.1.2025 %9 Viewpoint %J J Med Internet Res %G English %X To ensure that an eHealth technology fits with its intended users, other stakeholders, and the context within which it will be used, thorough development, implementation, and evaluation processes are necessary. The CeHRes (Centre for eHealth and Wellbeing Research) Roadmap is a framework that can help shape these processes. While it has been successfully used in research and practice, new developments and insights have arisen since the Roadmap’s first publication in 2011, not only within the domain of eHealth but also within the different disciplines in which the Roadmap is grounded. Because of these new developments and insights, a revision of the Roadmap was imperative. This paper aims to present the updated pillars and phases of the CeHRes Roadmap 2.0. The Roadmap was updated based on four types of sources: (1) experiences with its application in research; (2) literature reviews on eHealth development, implementation, and evaluation; (3) discussions with eHealth researchers; and (4) new insights and updates from relevant frameworks and theories. The updated pillars state that eHealth development, implementation, and evaluation (1) are ongoing and intertwined processes; (2) have a holistic approach in which context, people, and technology are intertwined; (3) consist of continuous evaluation cycles; (4) require active stakeholder involvement from the start; and (5) are based on interdisciplinary collaboration. The CeHRes Roadmap 2.0 consists of 5 interrelated phases, of which the first is the contextual inquiry, in which an overview of the involved stakeholders, the current situation, and points of improvement is created. The findings from the contextual inquiry are specified in the value specification, in which the foundation for the to-be-developed eHealth technology is created by formulating values and requirements, preliminarily selecting behavior change techniques and persuasive features, and initiating a business model. In the Design phase, the requirements are translated into several lo-fi and hi-fi prototypes that are iteratively tested with end users and other stakeholders. A version of the technology is rolled out in the Operationalization phase, using the business model and an implementation plan. In the Summative Evaluation phase, the impact, uptake, and working mechanisms are evaluated using a multimethod approach. All phases are interrelated by continuous formative evaluation cycles that ensure coherence between outcomes of phases and alignment with stakeholder needs. While the CeHRes Roadmap 2.0 consists of the same phases as the first version, the objectives and pillars have been updated and adapted, reflecting the increased emphasis on behavior change, implementation, and evaluation as a process. There is a need for more empirical studies that apply and reflect on the CeHRes Roadmap 2.0 to provide points of improvement because just as with any eHealth technology, the Roadmap has to be constantly improved based on the input of its users. %M 39805104 %R 10.2196/59601 %U https://www.jmir.org/2025/1/e59601 %U https://doi.org/10.2196/59601 %U http://www.ncbi.nlm.nih.gov/pubmed/39805104 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e55450 %T Service Attributes and Acceptability of Digital and Nondigital Depression Management Methods Among Individuals With Depressive Symptoms: Survey Study %A Auyeung,Larry %A Mak,Winnie W S %A Tsang,Ella Zoe %+ Department of Psychology, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China (Hong Kong), 852 31906792, larryauyeung@link.cuhk.edu.hk %K eHealth %K acceptability %K user preference %K diffusion of innovation %K mental health services %D 2024 %7 19.12.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Academic research on digital mental health tends to focus on its efficacy and effectiveness, with much less attention paid to user preferences and experiences in real-world settings. Objective: This study aims to analyze service characteristics that service users value and compare the extent to which various digital and nondigital mental health treatments and management methods fulfill users’ expectations. Methods: A total of 114 people with at least moderate levels of depressive symptoms (as measured by Patient Health Questionnaire–9 score ≥10) completed a web-based questionnaire measuring their awareness and adoption of digital mental health services and their valuation of 15 psychological service attributes, including effectiveness, credibility, waiting time, and more. They were also assessed on their expectations toward seven common mental health treatments and management methods, including (1) face-to-face psychological intervention, (2) medication, (3) guided internet-based psychological intervention, (4) face-to-face counseling service, (5) self-guided mental health apps for depression, (6) self-help bibliotherapy, and (7) psychological intervention via videoconferencing. Results: A Friedman test with a Dunn posttest showed the average importance rank of “effectiveness” was significantly higher than all other measured attributes. “Privacy,” “credibility,” and “cost” were ranked as equally important. Participants rated face-to-face psychological intervention the most effective management method, while other digital management methods were perceived as less effective. Medication was perceived as the least appealing method, while other methods were deemed equally appealing. Face-to-face psychological intervention, medication, and counseling were considered less satisfactory due to their higher costs and longer waiting times when compared to digital services. Repeated measures ANOVA showed some forms of management method were more likely to be adopted, including guided internet-based psychological intervention, psychological intervention via videoconferencing, face-to-face psychological intervention, and face-to-face counseling services provided by a counselor as compared to self-guided mobile apps, self-help bibliotherapy, and medication. Conclusions: The study highlights the importance of considering multiple service attributes beyond effectiveness in depression management methods, despite effectiveness being regarded as the most crucial factor using the rank method. Compared to nondigital services, digital services were identified as having specific strengths as perceived by users. Future dissemination and promotion efforts may focus on debunking myths of guided internet-based psychological intervention as a less effective option and promoting the particular service strengths of digital services. %M 39699956 %R 10.2196/55450 %U https://formative.jmir.org/2024/1/e55450 %U https://doi.org/10.2196/55450 %U http://www.ncbi.nlm.nih.gov/pubmed/39699956 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50491 %T Unlocking the Potential for Implementation of Equitable, Digitally Enabled Citizen Science: Multidisciplinary Digital Health Perspective %A Naccarella,Lucio %A Rawstorn,Jonathan Charles %A Kelly,Jaimon %A Quested,Eleanor %A Jenkinson,Stuart %A Kwasnicka,Dominika %+ Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie St Carlton, Victoria, Melbourne, 3010, Australia, 61 390355511, dom.kwasnicka@unimelb.edu.au %K citizen science %K digital health %K equity %K implementation science %K community %K research %K health inequality %K health equity %K health integration %K mental well-being %K well-being %D 2024 %7 10.12.2024 %9 Viewpoint %J J Med Internet Res %G English %X Citizen science is a community-based participatory research approach with an emphasis on addressing health disparities that is increasingly advocated by the community, researchers, and research funders. Digitally enabled methods can extend the potential of citizen science by enabling citizens to engage in real-time research processes, such as data collection, information sharing, interpreting, acting on data, and informing decision-making. However, the power of any citizen science lies in promoting health equity by providing equal opportunity for citizen engagement. Without appropriate attention to recognize and address equity, digital enablement of citizen science may exacerbate rather than ameliorate health inequalities. In this Viewpoint, we draw on our digital health research experience and perspectives to outline the practice of citizen science in the context of digital health—how it is operationalized, key advocated principles, and challenges. We also discuss citizen science in relation to health equity and implementation science, including emphasizing the importance of integrating health equity principles and frameworks, health equity implementation determinants, and digital determinants of health. We demonstrate how equity could be achieved by providing a working example in the context of a digitally enabled approach to improving social, physical, and mental well-being among people with disability and caregivers. %M 39657167 %R 10.2196/50491 %U https://www.jmir.org/2024/1/e50491 %U https://doi.org/10.2196/50491 %U http://www.ncbi.nlm.nih.gov/pubmed/39657167 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 3 %N %P e55833 %T Current State of Community-Driven Radiological AI Deployment in Medical Imaging %A Gupta,Vikash %A Erdal,Barbaros %A Ramirez,Carolina %A Floca,Ralf %A Genereaux,Bradley %A Bryson,Sidney %A Bridge,Christopher %A Kleesiek,Jens %A Nensa,Felix %A Braren,Rickmer %A Younis,Khaled %A Penzkofer,Tobias %A Bucher,Andreas Michael %A Qin,Ming Melvin %A Bae,Gigon %A Lee,Hyeonhoon %A Cardoso,M Jorge %A Ourselin,Sebastien %A Kerfoot,Eric %A Choudhury,Rahul %A White,Richard D %A Cook,Tessa %A Bericat,David %A Lungren,Matthew %A Haukioja,Risto %A Shuaib,Haris %+ Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL, 32224, United States, 1 904 953 2480, erdal.barbaros@mayo.edu %K radiology %K open-source %K radiology in practice %K deep learning %K artificial intelligence %K imaging informatics %K clinical deployment %K imaging %K medical informatics %K workflow %K operation %K implementation %K adoption %K taxonomy %K use case %K model %K integration %K machine learning %K mobile phone %D 2024 %7 9.12.2024 %9 Viewpoint %J JMIR AI %G English %X Artificial intelligence (AI) has become commonplace in solving routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. AI has been shown to improve efficiency in medical image generation, processing, and interpretation, and various such AI models have been developed across research laboratories worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. The goal of this paper is to give an overview of the intersection of AI and medical imaging landscapes. We also want to inform the readers about the importance of using standards in their radiology workflow and the challenges associated with deploying AI models in the clinical workflow. The main focus of this paper is to examine the existing condition of radiology workflow and identify the challenges hindering the implementation of AI in hospital settings. This report reflects extensive weekly discussions and practical problem-solving expertise accumulated over multiple years by industry experts, imaging informatics professionals, research scientists, and clinicians. To gain a deeper understanding of the requirements for deploying AI models, we introduce a taxonomy of AI use cases, supplemented by real-world instances of AI model integration within hospitals. We will also explain how the need for AI integration in radiology can be addressed using the Medical Open Network for AI (MONAI). MONAI is an open-source consortium for providing reproducible deep learning solutions and integration tools for radiology practice in hospitals. %M 39653370 %R 10.2196/55833 %U https://ai.jmir.org/2024/1/e55833 %U https://doi.org/10.2196/55833 %U http://www.ncbi.nlm.nih.gov/pubmed/39653370 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e60851 %T EyeMatics: An Ophthalmology Use Case Within the German Medical Informatics Initiative %A Varghese,Julian %A Schuster,Alexander %A Poschkamp,Broder %A Yildirim,Kemal %A Oehm,Johannes %A Berens,Philipp %A Müller,Sarah %A Gervelmeyer,Julius %A Koch,Lisa %A Hoffmann,Katja %A Sedlmayr,Martin %A Kakkassery,Vinodh %A Kohlbacher,Oliver %A Merle,David %A Bartz-Schmidt,Karl Ulrich %A Ueffing,Marius %A Stahl,Dana %A Leddig,Torsten %A Bialke,Martin %A Hampf,Christopher %A Hoffmann,Wolfgang %A Berthe,Sebastian %A Waltemath,Dagmar %A Walter,Peter %A Lipprandt,Myriam %A Röhrig,Rainer %A Storp,Jens Julian %A Zimmermann,Julian Alexander %A Holtrup,Lea %A Brix,Tobias %A Stahl,Andreas %A Eter,Nicole %K digital ophthalmology %K interoperability %K precision ophthalmology %K patient engagement %K Germany %K clinical use %K intravitreal %K injections %K eye %K treatment %K patient data %K framework %K AI %K artificial intelligence %K biomarker %K retinal %K scan %K user-centered %K observational %D 2024 %7 5.12.2024 %9 %J JMIR Med Inform %G English %X The EyeMatics project, embedded as a clinical use case in Germany’s Medical Informatics Initiative, is a large digital health initiative in ophthalmology. The objective is to improve the understanding of the treatment effects of intravitreal injections, the most frequent procedure to treat eye diseases. To achieve this, valuable patient data will be meaningfully integrated and visualized from different IT systems and hospital sites. EyeMatics emphasizes a governance framework that actively involves patient representatives, strictly implements interoperability standards, and employs artificial intelligence methods to extract biomarkers from tabular and clinical data as well as raw retinal scans. In this perspective paper, we delineate the strategies for user-centered implementation and health care–based evaluation in a multisite observational technology study. %R 10.2196/60851 %U https://medinform.jmir.org/2024/1/e60851 %U https://doi.org/10.2196/60851 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57754 %T Data Ownership in the AI-Powered Integrative Health Care Landscape %A Liu,Shuimei %A Guo,L Raymond %+ School of Juris Master, China University of Political Science and Law, 25 Xitucheng Rd, Hai Dian Qu, Beijing, 100088, China, 1 (734) 358 3970, shuiliu0802@alumni.iu.edu %K data ownership %K integrative healthcare %K artificial intelligence %K AI %K ownership %K data science %K governance %K consent %K privacy %K security %K access %K model %K framework %K transparency %D 2024 %7 19.11.2024 %9 Viewpoint %J JMIR Med Inform %G English %X In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care. %M 39560980 %R 10.2196/57754 %U https://medinform.jmir.org/2024/1/e57754 %U https://doi.org/10.2196/57754 %U http://www.ncbi.nlm.nih.gov/pubmed/39560980 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58933 %T A 25-Year Retrospective of Health IT Infrastructure Building: The Example of the Catalonia Region %A Piera-Jiménez,Jordi %A Carot-Sans,Gerard %A Ramiro-Pareta,Marina %A Nogueras,Maria Mercedes %A Folguera-Profitós,Júlia %A Ródenas,Pepi %A Jiménez-Rueda,Alba %A de Pando Navarro,Thais %A Mira Palacios,Josep Antoni %A Fajardo,Joan Carles %A Ustrell Campillo,Joan %A Vela,Emili %A Monterde,David %A Valero-Bover,Damià %A Bonet,Tara %A Tarrasó-Urios,Guillermo %A Cantenys-Sabà,Roser %A Fabregat-Fabregat,Pau %A Gómez Oliveros,Beatriz %A Berdún,Jesús %A Michelena,Xabier %A Cano,Isaac %A González-Colom,Rubèn %A Roca,Josep %A Solans,Oscar %A Pontes,Caridad %A Pérez-Sust,Pol %+ Catalan Health Service, Gran Via de les Corts Catalanes 587, Barcelona, 08007, Spain, 34 934643013, jpiera@catsalut.cat %K health ITs %K eHealth %K integrated care %K open platforms %K interoperability %K Catalonia %K digitalization %K health care structure %K health care delivery %K integrated pathway %K integrated treatment plan %K process management %D 2024 %7 18.11.2024 %9 Viewpoint %J J Med Internet Res %G English %X Over the past decades, health care systems have significantly evolved due to aging populations, chronic diseases, and higher-quality care expectations. Concurrently with the added health care needs, information and communications technology advancements have transformed health care delivery. Technologies such as telemedicine, electronic health records, and mobile health apps promise enhanced accessibility, efficiency, and patient outcomes, leading to more personalized, data-driven care. However, organizational, political, and cultural barriers and the fragmented approach to health information management are challenging the integration of these technologies to effectively support health care delivery. This fragmentation collides with the need for integrated care pathways that focus on holistic health and wellness. Catalonia (northeast Spain), a region of 8 million people with universal health care coverage and a single public health insurer but highly heterogeneous health care service providers, has experienced outstanding digitalization and integration of health information over the past 25 years, when the first transition from paper to digital support occurred. This Viewpoint describes the implementation of health ITs at a system level, discusses the hits and misses encountered in this journey, and frames this regional implementation within the global context. We present the architectures and use trends of the health information platforms over time. This provides insightful information that can be used by other systems worldwide in the never-ending transformation of health care structure and services. %M 39556831 %R 10.2196/58933 %U https://www.jmir.org/2024/1/e58933 %U https://doi.org/10.2196/58933 %U http://www.ncbi.nlm.nih.gov/pubmed/39556831 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e52675 %T Unintended Consequences of Data Sharing Under the Meaningful Use Program %A Willcockson,Irmgard Ursula %A Valdes,Ignacio Herman %K electronic health records %K EHR %K medical record %K interoperability %K health information interoperability %K clinical burden %K Medicare %K Medicaid %K reimbursement %K data science %K data governance %K data breach %K cybersecurity %K privacy %D 2024 %7 14.11.2024 %9 %J JMIR Med Inform %G English %X Interoperability has been designed to improve the quality and efficiency of health care. It allows the Centers for Medicare and Medicaid Services to collect data on quality measures as a part of the Meaningful Use program. Covered providers who fail to provide data have lower rates of reimbursement. Unintended consequences also arise at each step of the data collection process: (1) providers are not reimbursed for the extra time required to generate data; (2) patients do not have control over when and how their data are provided to or used by the government; and (3) large datasets increase the chances of an accidental data breach or intentional hacker attack. After detailing the issues, we describe several solutions, including an appropriate data use review board, which is designed to oversee certain aspects of the process and ensure accountability and transparency. %R 10.2196/52675 %U https://medinform.jmir.org/2024/1/e52675 %U https://doi.org/10.2196/52675 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e64226 %T Economics and Equity of Large Language Models: Health Care Perspective %A Nagarajan,Radha %A Kondo,Midori %A Salas,Franz %A Sezgin,Emre %A Yao,Yuan %A Klotzman,Vanessa %A Godambe,Sandip A %A Khan,Naqi %A Limon,Alfonso %A Stephenson,Graham %A Taraman,Sharief %A Walton,Nephi %A Ehwerhemuepha,Louis %A Pandit,Jay %A Pandita,Deepti %A Weiss,Michael %A Golden,Charles %A Gold,Adam %A Henderson,John %A Shippy,Angela %A Celi,Leo Anthony %A Hogan,William R %A Oermann,Eric K %A Sanger,Terence %A Martel,Steven %+ Children's Hospital of Orange County, 1201 W. La Veta Ave, Orange, CA, 92868, United States, 1 714 997 3000, Radha.Nagarajan@choc.org %K large language model %K LLM %K health care %K economics %K equity %K cloud service providers %K cloud %K health outcome %K implementation %K democratization %D 2024 %7 14.11.2024 %9 Viewpoint %J J Med Internet Res %G English %X Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways—training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)—as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care–related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably. %M 39541580 %R 10.2196/64226 %U https://www.jmir.org/2024/1/e64226 %U https://doi.org/10.2196/64226 %U http://www.ncbi.nlm.nih.gov/pubmed/39541580 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57035 %T Targeted Development and Validation of Clinical Prediction Models in Secondary Care Settings: Opportunities and Challenges for Electronic Health Record Data %A van Maurik,I S %A Doodeman,H J %A Veeger-Nuijens,B W %A Möhringer,R P M %A Sudiono,D R %A Jongbloed,W %A van Soelen,E %K clinical prediction model %K electronic health record %K targeted validation %K EHR %K EMR %K prediction models %K validation %K CPM %K secondary care %K machine learning %K artificial intelligence %K AI %D 2024 %7 24.10.2024 %9 %J JMIR Med Inform %G English %X Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called “targeted validation.” Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers the implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of CPMs in secondary care settings and discuss the potential and challenges of using electronic health record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured “big” datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: (1) involve a local EHR expert (clinician or nurse) in the data extraction process, (2) perform validity checks on the generated datasets, and (3) provide metadata on how variables were constructed from EHRs. These steps help to generate EHR datasets that are statistically powerful, of sufficient quality and replicable, and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice. %R 10.2196/57035 %U https://medinform.jmir.org/2024/1/e57035 %U https://doi.org/10.2196/57035 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e60402 %T 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 %A Fernando,Manasha %A Abell,Bridget %A McPhail,Steven M %A Tyack,Zephanie %A Tariq,Amina %A Naicker,Sundresan %+ Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Q Block, 60 Musk Avenue, Brisbane, 4059, Australia, 61 3138 6454, sundresan.naicker@qut.edu.au %K medical informatics %K adoption and implementation %K behavior %K health systems %D 2024 %7 17.10.2024 %9 Original Paper %J JMIR Med Inform %G English %X 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. %M 39419497 %R 10.2196/60402 %U https://medinform.jmir.org/2024/1/e60402 %U https://doi.org/10.2196/60402 %U http://www.ncbi.nlm.nih.gov/pubmed/39419497 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58478 %T Practical Applications of Large Language Models for Health Care Professionals and Scientists %A Reis,Florian %A Lenz,Christian %A Gossen,Manfred %A Volk,Hans-Dieter %A Drzeniek,Norman Michael %K artificial intelligence %K healthcare %K chatGPT %K large language model %K prompting %K LLM %K applications %K AI %K scientists %K physicians %K health care %D 2024 %7 5.9.2024 %9 %J JMIR Med Inform %G English %X With the popularization of large language models (LLMs), strategies for their effective and safe usage in health care and research have become increasingly pertinent. Despite the growing interest and eagerness among health care professionals and scientists to exploit the potential of LLMs, initial attempts may yield suboptimal results due to a lack of user experience, thus complicating the integration of artificial intelligence (AI) tools into workplace routine. Focusing on scientists and health care professionals with limited LLM experience, this viewpoint article highlights and discusses 6 easy-to-implement use cases of practical relevance. These encompass customizing translations, refining text and extracting information, generating comprehensive overviews and specialized insights, compiling ideas into cohesive narratives, crafting personalized educational materials, and facilitating intellectual sparring. Additionally, we discuss general prompting strategies and precautions for the implementation of AI tools in biomedicine. Despite various hurdles and challenges, the integration of LLMs into daily routines of physicians and researchers promises heightened workplace productivity and efficiency. %R 10.2196/58478 %U https://medinform.jmir.org/2024/1/e58478 %U https://doi.org/10.2196/58478 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58080 %T Exploring Impediments Imposed by the Medical Device Regulation EU 2017/745 on Software as a Medical Device %A Svempe,Liga %+ Faculty of Social Sciences, Riga Stradins University, Dzirciema 16, Riga, LV1007, Latvia, 371 67409120, liga.svempe@rsu.edu.lv %K software %K artificial intelligence %K medical device regulation %K rights %K digital health %D 2024 %7 5.9.2024 %9 Viewpoint %J JMIR Med Inform %G English %X In light of rapid technological advancements, the health care sector is undergoing significant transformation with the continuous emergence of novel digital solutions. Consequently, regulatory frameworks must continuously adapt to ensure their main goal to protect patients. In 2017, the new Medical Device Regulation (EU) 2017/745 (MDR) came into force, bringing more complex requirements for development, launch, and postmarket surveillance. However, the updated regulation considerably impacts the manufacturers, especially small- and medium-sized enterprises, and consequently, the accessibility of medical devices in the European Union market, as many manufacturers decide to either discontinue their products, postpone the launch of new innovative solutions, or leave the European Union market in favor of other regions such as the United States. This could lead to reduced health care quality and slower industry innovation efforts. Effective policy calibration and collaborative efforts are essential to mitigate these effects and promote ongoing advancements in health care technologies in the European Union market. This paper is a narrative review with the objective of exploring hindering factors to software as a medical device development, launch, and marketing brought by the new regulation. It exclusively focuses on the factors that engender obstacles. Related regulations, directives, and proposals were discussed for comparison and further analysis. %M 39235850 %R 10.2196/58080 %U https://medinform.jmir.org/2024/1/e58080 %U https://doi.org/10.2196/58080 %U http://www.ncbi.nlm.nih.gov/pubmed/39235850 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58548 %T Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries %A Julian,Guilherme Silva %A Shau,Wen-Yi %A Chou,Hsu-Wen %A Setia,Sajita %+ Executive Office, Transform Medical Communications Limited, 184 Glasgow Street, Wanganui, 4500, New Zealand, 64 276175433, sajita.setia@transform-medcomms.com %K Asia %K electronic medical records %K EMR %K health care databases %K health technology assessment %K HTA %K real-world data %K real-world evidence %D 2024 %7 15.8.2024 %9 Viewpoint %J JMIR Med Inform %G English %X The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: “Solo Scholars”—nations with relatively self-sufficient RWD research systems—and “Global Collaborators”—countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region. %M 39026427 %R 10.2196/58548 %U https://medinform.jmir.org/2024/1/e58548 %U https://doi.org/10.2196/58548 %U http://www.ncbi.nlm.nih.gov/pubmed/39026427 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59005 %T The Second Life Metaverse and Its Usefulness in Medical Education After a Quarter of a Century %A Sendra-Portero,Francisco %A Lorenzo-Álvarez,Rocío %A Rudolphi-Solero,Teodoro %A Ruiz-Gómez,Miguel José %+ Department of Radiology and Physical Medicine, Facultad de Medicina, Universidad de Málaga, Bvd. Luis Pasteur, 32. 20071, Málaga, 29071, Spain, 34 952131653, sendra@uma.es %K medical education %K medical students %K postgraduate %K computer simulation %K virtual worlds %K metaverse %D 2024 %7 6.8.2024 %9 Viewpoint %J J Med Internet Res %G English %X The immersive virtual world platform Second Life (SL) was conceived 25 years ago, when Philip Rosedale founded Linden Lab in 1999 with the intention of developing computing hardware that would allow people to immerse themselves in a virtual world. This initial effort was transformed 4 years later into SL, a universally accessible virtual world centered on the user, with commercial transactions and even its own virtual currency, which fully connects with the concept of the metaverse, recently repopularized after the statements of the chief executive officer of Meta (formerly Facebook) in October 2021. SL is considered the best known virtual environment among higher education professionals. This paper aimed to review medical education in the SL metaverse; its evolution; and its possibilities, limitations, and future perspectives, focusing especially on medical education experiences during undergraduate, residency, and continuing medical education. The concept of the metaverse and virtual worlds was described, making special reference to SL and its conceptual philosophy, historical evolution, and technical aspects and capabilities for higher education. A narrative review of the existing literature was performed, including at the same time a point of view from our teaching team after an uninterrupted practical experience of undergraduate and postgraduate medical education in the last 13 years with >4000 users and >10 publications on the subject. From an educational point of view, SL has the advantages of being available 24/7 and creating in the student the important feeling of “being there” and of copresence. This, together with the reproduction of the 3D world, real-time interaction, and the quality of voice communication, makes the immersive experiences unique, generating engagement and a fluid interrelation of students with each other and with their teachers. Various groups of researchers in medical education have developed experiences during these years, which have shown that courses, seminars, workshops and conferences, problem-based learning experiences, evaluations, teamwork, gamification, medical simulation, and virtual objective structured clinical examinations can be successfully carried out. Acceptance from students and faculty is generally positive, recognizing its usefulness for undergraduate medical education and continuing medical education. In the 25 years since its conception, SL has proven to be a virtual platform that connects with the concept of the metaverse, an interconnected, open, and globally accessible system that all humans can access to socialize or share products for free or using a virtual currency. SL remains active and technologically improved since its creation. It is necessary to continue carrying out educational experiences, outlining the organization, objectives, and content and measuring the actual educational impact to make SL a tool of more universal use. %M 39106480 %R 10.2196/59005 %U https://www.jmir.org/2024/1/e59005 %U https://doi.org/10.2196/59005 %U http://www.ncbi.nlm.nih.gov/pubmed/39106480 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50355 %T 5G Key Technologies for Helicopter Aviation Medical Rescue %A Han Sr,Wei %A Li 2nd,Yuanting %A Chen 3rd,Changgen %A Huang,Danni %A Wang,Junchao %A Li,Xiang %A Ji,Zhongliang %A Li,Qin %A Li,Zhuang %+ Emergency Department, Shenzhen University General Hospital, 1098 Xueyuan Avenue, Nanshan District, Shenzhen, 518055, China, 86 18813980300, sugh_hanwei@szu.edu.cn %K low airspace %K helicopters %K medical aid %K 5G technology %K aeronautical engineering %D 2024 %7 1.8.2024 %9 Viewpoint %J J Med Internet Res %G English %X Rapid global population growth and urbanization have heightened the demand for emergency medical rescue, with helicopter medical rescue emerging as an effective solution. The advent of 5G communication technology, characterized by large bandwidth, low latency, and high reliability, offers substantial promise in enhancing the efficiency and quality of helicopter rescue operations. However, the full integration of 5G technology into helicopter emergency medical services is still in its nascent stages and requires further development. In this viewpoint, we present our experience from the Shenzhen University General Hospital of the application of 5G low-altitude network communication technology, body area network disease sensing technology, and 5G air-ground collaborative rapid diagnosis and treatment technology in aeromedical rescue. We consider that the 5G air-to-ground collaborative rapid diagnosis and treatment technology enables high-quality remote consultation, enhancing emergency medical rescue and providing strong support for future rescue operations. %M 39088814 %R 10.2196/50355 %U https://www.jmir.org/2024/1/e50355 %U https://doi.org/10.2196/50355 %U http://www.ncbi.nlm.nih.gov/pubmed/39088814 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e55933 %T Impact of Large Language Models on Medical Education and Teaching Adaptations %A Zhui,Li %A Yhap,Nina %A Liping,Liu %A Zhengjie,Wang %A Zhonghao,Xiong %A Xiaoshu,Yuan %A Hong,Cui %A Xuexiu,Liu %A Wei,Ren %K large language models %K medical education %K opportunities %K challenges %K critical thinking %K educator %D 2024 %7 25.7.2024 %9 %J JMIR Med Inform %G English %X This viewpoint article explores the transformative role of large language models (LLMs) in the field of medical education, highlighting their potential to enhance teaching quality, promote personalized learning paths, strengthen clinical skills training, optimize teaching assessment processes, boost the efficiency of medical research, and support continuing medical education. However, the use of LLMs entails certain challenges, such as questions regarding the accuracy of information, the risk of overreliance on technology, a lack of emotional recognition capabilities, and concerns related to ethics, privacy, and data security. This article emphasizes that to maximize the potential of LLMs and overcome these challenges, educators must exhibit leadership in medical education, adjust their teaching strategies flexibly, cultivate students’ critical thinking, and emphasize the importance of practical experience, thus ensuring that students can use LLMs correctly and effectively. By adopting such a comprehensive and balanced approach, educators can train health care professionals who are proficient in the use of advanced technologies and who exhibit solid professional ethics and practical skills, thus laying a strong foundation for these professionals to overcome future challenges in the health care sector. %R 10.2196/55933 %U https://medinform.jmir.org/2024/1/e55933 %U https://doi.org/10.2196/55933 %0 Journal Article %@ 2373-6658 %I JMIR Publications %V 8 %N %P e60116 %T Centralized Pump Monitoring System: Perception on Utility and Workflows by Nurses in a Tertiary Hospital %A Chindamorragot,Naruemol %A Suitthimeathegorn,Orawan %A Garg,Amit %+ Medical Affairs, Terumo Asia Holdings Pte. Ltd., 300 Beach Road, #37-01 The Concourse, Singapore, 199555, Singapore, 65 92991056, orawan_suitthimeathegorn@terumo.co.jp %K infusion management %K nurse efficiency %K pump monitoring system %K nurse attrition %D 2024 %7 24.7.2024 %9 Viewpoint %J Asian Pac Isl Nurs J %G English %X Nurses play a key role in providing in-hospital care to patients. Worldwide, there has been a shortage of nursing staff, putting enormous strain on the existing nursing workforce physically and mentally. A vicious cycle of demanding workplaces exacerbated by perennial shortages leads to attrition and high staff turnover. A centralized, automated infusion pump monitoring system optimizes and augments nurses’ performance in the hospital by cutting down on nurse visits to the patient’s bedside for every matter, whether significant or insignificant. This viewpoint intends to highlight that by filtering out the noise effectively, nurses can focus on improving patient outcome–led interventions and enhancing the quality of care. %M 39047286 %R 10.2196/60116 %U https://apinj.jmir.org/2024/1/e60116 %U https://doi.org/10.2196/60116 %U http://www.ncbi.nlm.nih.gov/pubmed/39047286 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e54951 %T Probing the Role of Digital Payment Solutions in Gambling Behavior: Preliminary Results From an Exploratory Focus Group Session With Problem Gamblers %A Lakew,Nathan %A Jonsson,Jakob %A Lindner,Philip %+ Department of Clinical Neuroscience, Karolinska Institutet, Norra Stationsgatan 69, Plan 7, Stockholm, 113 64, Sweden, 46 852483391, nathan.lakew@ki.se %K digital payment solutions %K online gambling behavior %K sociotechnical %K subjective experience %K focus group %D 2024 %7 23.7.2024 %9 Original Paper %J JMIR Hum Factors %G English %X 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. %M 39042438 %R 10.2196/54951 %U https://humanfactors.jmir.org/2024/1/e54951 %U https://doi.org/10.2196/54951 %U http://www.ncbi.nlm.nih.gov/pubmed/39042438 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e54590 %T Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline %A Lamer,Antoine %A Saint-Dizier,Chloé %A Paris,Nicolas %A Chazard,Emmanuel %K data reuse %K data lake %K data warehouse %K feature extraction %K datamart %K feature store %D 2024 %7 17.7.2024 %9 %J JMIR Med Inform %G English %X The growing adoption and use of health information technology has generated a wealth of clinical data in electronic format, offering opportunities for data reuse beyond direct patient care. However, as data are distributed across multiple software, it becomes challenging to cross-reference information between sources due to differences in formats, vocabularies, and technologies and the absence of common identifiers among software. To address these challenges, hospitals have adopted data warehouses to consolidate and standardize these data for research. Additionally, as a complement or alternative, data lakes store both source data and metadata in a detailed and unprocessed format, empowering exploration, manipulation, and adaptation of the data to meet specific analytical needs. Subsequently, datamarts are used to further refine data into usable information tailored to specific research questions. However, for efficient analysis, a feature store is essential to pivot and denormalize the data, simplifying queries. In conclusion, while data warehouses are crucial, data lakes, datamarts, and feature stores play essential and complementary roles in facilitating data reuse for research and analysis in health care. %R 10.2196/54590 %U https://medinform.jmir.org/2024/1/e54590 %U https://doi.org/10.2196/54590 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e50437 %T Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice %A Faust,Louis %A Wilson,Patrick %A Asai,Shusaku %A Fu,Sunyang %A Liu,Hongfang %A Ruan,Xiaoyang %A Storlie,Curt %+ Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, United States, 1 (507) 284 2511, Faust.Louis@mayo.edu %K artificial intelligence %K machine learning %K implementation science %K quality control %K monitoring %K patient safety %D 2024 %7 28.6.2024 %9 Viewpoint %J JMIR Med Inform %G English %X Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team’s technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation. %M 38941140 %R 10.2196/50437 %U https://medinform.jmir.org/2024/1/e50437 %U https://doi.org/10.2196/50437 %U http://www.ncbi.nlm.nih.gov/pubmed/38941140 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58491 %T AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine %A Lu,Linken %A Lu,Tangsheng %A Tian,Chunyu %A Zhang,Xiujun %+ School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei Province, 063210, China, 86 0315 8805970, zhxj@ncst.edu.cn %K traditional Chinese medicine %K TCM %K artificial intelligence %K AI %K diagnosis %D 2024 %7 28.6.2024 %9 Viewpoint %J JMIR Med Inform %G English %X The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new frontier that demonstrates the promise of combining the advantages of ancient healing practices with cutting-edge advancements in modern technology. TCM, which is a holistic medical system with >2000 years of empirical support, uses unique diagnostic methods such as inspection, auscultation and olfaction, inquiry, and palpation. AI is the simulation of human intelligence processes by machines, especially via computer systems. TCM is experience oriented, holistic, and subjective, and its combination with AI has beneficial effects, which presumably arises from the perspectives of diagnostic accuracy, treatment efficacy, and prognostic veracity. The role of AI in TCM is highlighted by its use in diagnostics, with machine learning enhancing the precision of treatment through complex pattern recognition. This is exemplified by the greater accuracy of TCM syndrome differentiation via tongue images that are analyzed by AI. However, integrating AI into TCM also presents multifaceted challenges, such as data quality and ethical issues; thus, a unified strategy, such as the use of standardized data sets, is required to improve AI understanding and application of TCM principles. The evolution of TCM through the integration of AI is a key factor for elucidating new horizons in health care. As research continues to evolve, it is imperative that technologists and TCM practitioners collaborate to drive innovative solutions that push the boundaries of medical science and honor the profound legacy of TCM. We can chart a future course wherein AI-augmented TCM practices contribute to more systematic, effective, and accessible health care systems for all individuals. %M 38941141 %R 10.2196/58491 %U https://medinform.jmir.org/2024/1/e58491 %U https://doi.org/10.2196/58491 %U http://www.ncbi.nlm.nih.gov/pubmed/38941141 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e51350 %T How to Elucidate Consent-Free Research Use of Medical Data: A Case for “Health Data Literacy” %A Richter,Gesine %A Krawczak,Michael %K health data literacy %K informed consent %K broad consent %K data sharing %K data collection %K data donation %K data linkage %K personal health data %D 2024 %7 18.6.2024 %9 %J JMIR Med Inform %G English %X The extensive utilization of personal health data is one of the key success factors of modern medical research. Obtaining consent to the use of such data during clinical care, however, bears the risk of low and unequal approval rates and risk of consequent methodological problems in the scientific use of the data. In view of these shortcomings, and of the proven willingness of people to contribute to medical research by sharing personal health data, the paradigm of informed consent needs to be reconsidered. The European General Data Protection Regulation gives the European member states considerable leeway with regard to permitting the research use of health data without consent. Following this approach would however require alternative offers of information that compensate for the lack of direct communication with experts during medical care. We therefore introduce the concept of “health data literacy,” defined as the capacity to find, understand, and evaluate information about the risks and benefits of the research use of personal health data and to act accordingly. Specifically, health data literacy includes basic knowledge about the goals and methods of data-rich medical research and about the possibilities and limits of data protection. Although the responsibility for developing the necessary resources lies primarily with those directly involved in data-rich medical research, improving health data literacy should ultimately be of concern to everyone interested in the success of this type of research. %R 10.2196/51350 %U https://medinform.jmir.org/2024/1/e51350 %U https://doi.org/10.2196/51350 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e56572 %T A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection %A Nkoy,Flory L %A Stone,Bryan L %A Zhang,Yue %A Luo,Gang %+ Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican Street, Building C, Box 358047, Seattle, WA, 98195, United States, 1 2062214596, gangluo@cs.wisc.edu %K asthma %K causal inference %K forecasting %K machine learning %K decision support %K drug %K drugs %K pharmacy %K pharmacies %K pharmacology %K pharmacotherapy %K pharmaceutic %K pharmaceutics %K pharmaceuticals %K pharmaceutical %K medication %K medications %K medication selection %K respiratory %K pulmonary %K forecast %K ICS %K inhaled corticosteroid %K inhaler %K inhaled %K corticosteroid %K corticosteroids %K artificial intelligence %K personalized %K customized %D 2024 %7 17.4.2024 %9 Viewpoint %J JMIR Med Inform %G English %X Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources. %M 38630536 %R 10.2196/56572 %U https://medinform.jmir.org/2024/1/e56572 %U https://doi.org/10.2196/56572 %U http://www.ncbi.nlm.nih.gov/pubmed/38630536 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e55499 %T Impact of Electronic Health Record Use on Cognitive Load and Burnout Among Clinicians: Narrative Review %A Asgari,Elham %A Kaur,Japsimar %A Nuredini,Gani %A Balloch,Jasmine %A Taylor,Andrew M %A Sebire,Neil %A Robinson,Robert %A Peters,Catherine %A Sridharan,Shankar %A Pimenta,Dominic %+ Tortus AI, 193-197 High Holborn, London, WC1V 7BD, United Kingdom, 44 7763891802, asgelham@gmail.com %K electronic health record %K cognitive load %K burnout %K technology %K clinician %D 2024 %7 12.4.2024 %9 Viewpoint %J JMIR Med Inform %G English %X The cognitive load theory suggests that completing a task relies on the interplay between sensory input, working memory, and long-term memory. Cognitive overload occurs when the working memory’s limited capacity is exceeded due to excessive information processing. In health care, clinicians face increasing cognitive load as the complexity of patient care has risen, leading to potential burnout. Electronic health records (EHRs) have become a common feature in modern health care, offering improved access to data and the ability to provide better patient care. They have been added to the electronic ecosystem alongside emails and other resources, such as guidelines and literature searches. Concerns have arisen in recent years that despite many benefits, the use of EHRs may lead to cognitive overload, which can impact the performance and well-being of clinicians. We aimed to review the impact of EHR use on cognitive load and how it correlates with physician burnout. Additionally, we wanted to identify potential strategies recommended in the literature that could be implemented to decrease the cognitive burden associated with the use of EHRs, with the goal of reducing clinician burnout. Using a comprehensive literature review on the topic, we have explored the link between EHR use, cognitive load, and burnout among health care professionals. We have also noted key factors that can help reduce EHR-related cognitive load, which may help reduce clinician burnout. The research findings suggest that inadequate efforts to present large amounts of clinical data to users in a manner that allows the user to control the cognitive burden in the EHR and the complexity of the user interfaces, thus adding more “work” to tasks, can lead to cognitive overload and burnout; this calls for strategies to mitigate these effects. Several factors, such as the presentation of information in the EHR, the specialty, the health care setting, and the time spent completing documentation and navigating systems, can contribute to this excess cognitive load and result in burnout. Potential strategies to mitigate this might include improving user interfaces, streamlining information, and reducing documentation burden requirements for clinicians. New technologies may facilitate these strategies. The review highlights the importance of addressing cognitive overload as one of the unintended consequences of EHR adoption and potential strategies for mitigation, identifying gaps in the current literature that require further exploration. %M 38607672 %R 10.2196/55499 %U https://medinform.jmir.org/2024/1/e55499 %U https://doi.org/10.2196/55499 %U http://www.ncbi.nlm.nih.gov/pubmed/38607672 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e51138 %T A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health %A Washington,Peter %+ Information and Computer Sciences, University of Hawaii at Manoa, 1680 East-West Road, Honolulu, HI, 96822, United States, pyw@hawaii.edu %K crowdsourcing %K digital medicine %K human-in-the-loop %K human in the loop %K human-AI collaboration %K machine learning %K precision health %K artificial intelligence %K AI %D 2024 %7 11.4.2024 %9 Viewpoint %J J Med Internet Res %G English %X Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care. %M 38602750 %R 10.2196/51138 %U https://www.jmir.org/2024/1/e51138 %U https://doi.org/10.2196/51138 %U http://www.ncbi.nlm.nih.gov/pubmed/38602750 %0 Journal Article %@ 2562-0959 %I JMIR Publications %V 7 %N %P e51267 %T Social Media Use in Dermatology in Turkey: Challenges and Tips for Patient Health %A Karadag,Ayse Serap %A Kandi,Basak %A Sanlı,Berna %A Ulusal,Hande %A Basusta,Hasan %A Sener,Seray %A Calıka,Sinem %+ Department of Dermatology, Medical School of Istanbul Arel University, Türkoba, Erguvan Sk No: 26, Istanbul, 34537, Turkey, 90 533 655 22 60, karadagaserap@gmail.com %K social media %K dermatology %K internet %K health promotion %K patient education %K Instagram %K YouTube %K online social networking %K social networking %K Turkey %K patient health %K skin %K skin disease %K skincare %K cosmetics %K digital communication %K misinformation %D 2024 %7 28.3.2024 %9 Viewpoint %J JMIR Dermatol %G English %X Social media has established its place in our daily lives, especially with the advent of the COVID-19 pandemic. It has become the leading source of information for dermatological literacy on various topics, ranging from skin diseases to everyday skincare and cosmetic purposes in the present digital era. Accumulated evidence indicates that accurate medical content constitutes only a tiny fraction of the exponentially growing dermatological information on digital platforms, highlighting an unmet patient need for access to evidence-based information on social media. However, there have been no recent local publications from Turkey analyzing and assessing the key elements in raising dermatological literacy and awareness in digital communication for patients. To the best of our knowledge, this study is the first collaborative work between health care professionals and a social media specialist in the medical literature. Furthermore, it represents the first author-initiated implementation science attempt focusing on the use of social media in addressing dermatological problems, with the primary end point of increasing health literacy and patient benefits. The multidisciplinary expert panel was formed by 4 dermatologists with academic credentials and significant influence in public health and among patients on digital platforms. A social media specialist, who serves as a guest lecturer on “How social media works” at Istanbul Technical University, Turkey, was invited to the panel as an expert on digital communication. The panel members had a kickoff meeting to establish the context for the discussion points. The context of the advisory board meeting was outlined under 5 headlines. Two weeks later, the panel members presented their social media account statistics, defined the main characteristics of dermatology patients on social media, and discussed their experiences with patients on digital platforms. These discussions were organized under the predefined headlines and in line with the current literature. We aimed to collect expert opinions on identifying the main characteristics of individuals interested in dermatological topics and to provide recommendations to help dermatologists increase evidence-based dermatological content on social media. Additionally, experts discussed paradigms for dermatological outreach and the role of dermatologists in reducing misleading information on digital platforms in Turkey. The main concluding remark of this study is that dermatologists should enhance their social media presence to increase evidence-based knowledge by applying the principles of patient-physician communication on digital platforms while maintaining a professional stance. To achieve this goal, dermatologists should share targeted scientific content after increasing their knowledge about the operational rules of digital channels. This includes correctly identifying the needs of those seeking information on social media and preparing a sustainable social media communication plan. This viewpoint reflects Turkish dermatologists’ experiences with individuals searching for dermatological information on local digital platforms; therefore, the applicability of recommendations may be limited and should be carefully considered. %M 38546714 %R 10.2196/51267 %U https://derma.jmir.org/2024/1/e51267 %U https://doi.org/10.2196/51267 %U http://www.ncbi.nlm.nih.gov/pubmed/38546714 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e49208 %T The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring %A Kim,Meelim %A Patrick,Kevin %A Nebeker,Camille %A Godino,Job %A Stein,Spencer %A Klasnja,Predrag %A Perski,Olga %A Viglione,Clare %A Coleman,Aaron %A Hekler,Eric %+ Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Dr, MC0811, La Jolla, CA, 92093, United States, 1 858 429 9370, ehekler@ucsd.edu %K accessible %K decision making %K decision %K decision-based evidence-making %K development %K digital therapeutics %K medication adherence %K monitoring %K pharmaceuticals %K public health %K real-world data %K real-world evidence %K safe %K testing %K therapeutics %D 2024 %7 5.3.2024 %9 Viewpoint %J J Med Internet Res %G English %X Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health. %M 38441954 %R 10.2196/49208 %U https://www.jmir.org/2024/1/e49208 %U https://doi.org/10.2196/49208 %U http://www.ncbi.nlm.nih.gov/pubmed/38441954 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e49022 %T Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics %A Bhargava,Hansa %A Salomon,Carmela %A Suresh,Srinivasan %A Chang,Anthony %A Kilian,Rachel %A Stijn,Diana van %A Oriol,Albert %A Low,Daniel %A Knebel,Ashley %A Taraman,Sharief %+ Cognoa, Inc, 2185 Park Blvd, Palo Alto, CA, 94306, United States, 1 8664264622, carmela.salomon@cognoa.com %K artificial intelligence %K pediatrics %K autism spectrum disorder %K ASD %K disparities %K pediatric %K youth %K child %K children %K autism %K autistic %K barrier %K barriers %K clinical application %K clinical applications %K professional development %K continuing education %K continuing medical education %K CME %K implementation %D 2024 %7 29.2.2024 %9 Viewpoint %J J Med Internet Res %G English %X Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine. %M 38421690 %R 10.2196/49022 %U https://www.jmir.org/2024/1/e49022 %U https://doi.org/10.2196/49022 %U http://www.ncbi.nlm.nih.gov/pubmed/38421690 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e41670 %T Extended Reality—New Opportunity for People With Disability? Practical and Ethical Considerations %A Stendal,Karen %A Bernabe,Rosemarie D L C %+ Department of Business, Marketing and Law, University of South-Eastern Norway, Bredalsveien 14, Honefoss, 3502, Norway, 47 31009477, karen.stendal@usn.no %K extended reality %K virtual worlds %K virtual reality %K disability %K practical %K ethical %K technology %K virtual %K reality %K development %K research %K challenges %D 2024 %7 13.2.2024 %9 Viewpoint %J J Med Internet Res %G English %X Since the introduction of virtual environments in the 70s, technologies have moved through virtual reality, mixed reality, and augmented reality into extended reality (XR). This development is promising for various groups. Previous research has shown people with disability benefiting from using technology in social and professional settings. Technology has offered people with disability the opportunity to communicate, interact, participate, and build new relationships. However, we do not know what impact XR has or will have and whether it will offer new opportunities for people with disability. This paper aims to indicate potential opportunities and challenges afforded by XR to people with disability. We offer reflections on the opportunities as well as the ethical considerations needed when introducing immersive technologies to a marginalized group. %M 38349731 %R 10.2196/41670 %U https://www.jmir.org/2024/1/e41670 %U https://doi.org/10.2196/41670 %U http://www.ncbi.nlm.nih.gov/pubmed/38349731 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e51980 %T Defining the Dimensions of Diversity to Promote Inclusion in the Digital Era of Health Care: A Lexicon %A Sharma,Yashoda %A Saha,Anindita %A Goldsack,Jennifer C %+ Digital Medicine Society, 90 Canal Street, 4th Floor, Boston, MA, 02114, United States, 1 765 234 3463, yashoda@dimesociety.org %K digital medicine %K inclusion %K digital health technology/product %K digital health %K digital technology %K health care system %K innovation %K equity %K quality %K disparity %K digital era %K digital access %K digital literacy %D 2024 %7 9.2.2024 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The pandemic provided a stark reminder of the inequities faced by populations historically marginalized by the health care system and accelerated the adoption of digital health technologies to drive innovation. Digital health technologies’ purported promises to reduce inefficiencies and costs, improve access and health outcomes, and empower patients add a new level of urgency to health equity. As conventional medicine shifts toward digital medicine, we have the opportunity to intentionally develop and deploy digital health technologies with an inclusion focus. The first step is ensuring that the multiple dimensions of diversity are captured. We propose a lexicon that encompasses elements critical for implementing an inclusive approach to advancing health care quality and health services research in the digital era. %M 38335013 %R 10.2196/51980 %U https://publichealth.jmir.org/2024/1/e51980 %U https://doi.org/10.2196/51980 %U http://www.ncbi.nlm.nih.gov/pubmed/38335013 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e52080 %T The Current Status and Promotional Strategies for Cloud Migration of Hospital Information Systems in China: Strengths, Weaknesses, Opportunities, and Threats Analysis %A Xu,Jian %+ Department of Health Policy, Beijing Municipal Health Big Data and Policy Research Center, Building 1, Number 6 Daji Street, Tongzhou District, Beijing, 101160, China, 86 01055532146, _xujian@163.com %K hospital information system %K HIS %K cloud computing %K cloud migration %K Strengths, Weaknesses, Opportunities, and Threats analysis %D 2024 %7 5.2.2024 %9 Viewpoint %J JMIR Med Inform %G English %X 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. %M 38315519 %R 10.2196/52080 %U https://medinform.jmir.org/2024/1/e52080 %U https://doi.org/10.2196/52080 %U http://www.ncbi.nlm.nih.gov/pubmed/38315519 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e53516 %T Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition %A Koonce,Taneya Y %A Giuse,Dario A %A Williams,Annette M %A Blasingame,Mallory N %A Krump,Poppy A %A Su,Jing %A Giuse,Nunzia B %+ Center for Knowledge Management, Vanderbilt University Medical Center, 3401 West End, Suite 304, Nashville, TN, 37203, United States, 1 6159365790, taneya.koonce@vumc.org %K natural language processing %K electronic health records %K machine learning %K data mining %K knowledge management %K NLP %D 2024 %7 30.1.2024 %9 Viewpoint %J JMIR Med Inform %G English %X Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC’s electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies. %M 38289670 %R 10.2196/53516 %U https://medinform.jmir.org/2024/1/e53516 %U https://doi.org/10.2196/53516 %U http://www.ncbi.nlm.nih.gov/pubmed/38289670 %0 Journal Article %@ 2291-9694 %I %V 11 %N %P e53112 %T A Call to Reconsider a Nationwide Electronic Health Record System: Correcting the Failures of the National Program for IT %A Morris,James Seymour %K electronic health record %K EHR %K medical record linkage %K health information interoperability %K health information management %K health information systems %K information systems %K interoperability %K health records %K medical records %K national %D 2023 %7 28.12.2023 %9 %J JMIR Med Inform %G English %X The National Programme for IT (NPfIT) was launched in 2005 to implement 7 nationwide IT services across the National Health Service (NHS). Despite the success of many of these designated “deliverables,” the establishment of a single nationwide electronic health record (EHR) system never fully materialized. As a result, NHS medical records are now stored using a diverse array of alternate EHR systems, which frequently restricts health care practitioners from accessing extensive portions of their patients’ notes. This not only limits their ability to make well-informed clinical decisions but also impacts the quality of care they are able to provide. This article assesses the medical, economic, and bureaucratic implications of an NHS-wide EHR system. Additionally, it explores how the shortcomings of the NPfIT should be addressed when attempting to introduce such a system in the future. %R 10.2196/53112 %U https://medinform.jmir.org/2023/1/e53112 %U https://doi.org/10.2196/53112 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e49301 %T The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices %A Schwab,Julian D %A Werle,Silke D %A Hühne,Rolf %A Spohn,Hannah %A Kaisers,Udo X %A Kestler,Hans A %+ Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany, 49 731 500 24500, hans.kestler@uni-ulm.de %K semantic terminology %K semantic %K terminology %K terminologies %K data linkage %K interoperability %K data exchange %K SNOMED CT %K LOINC %K eHealth %K patient-reported outcome questionnaires %K requirement for standards %K standard %K standards %K PRO %K PROM %K patient reported %D 2023 %7 22.12.2023 %9 Viewpoint %J JMIR Med Inform %G English %X Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient’s feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them. %M 38133917 %R 10.2196/49301 %U https://medinform.jmir.org/2023/1/e49301 %U https://doi.org/10.2196/49301 %U http://www.ncbi.nlm.nih.gov/pubmed/38133917 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44265 %T The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions %A Nourse,Rebecca %A Dingler,Tilman %A Kelly,Jaimon %A Kwasnicka,Dominika %A Maddison,Ralph %+ School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, 3125, Australia, 61 0392443075, rnourse@deakin.edu.au %K smart home %K health %K chronic condition %K chronic illness %K digital health %K technology %K behavior change %K wearable %K smart technology %K smart health %K economic %K cost %K security %K data storage %K implementation %D 2023 %7 18.12.2023 %9 Viewpoint %J J Med Internet Res %G English %X The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem. %M 38109188 %R 10.2196/44265 %U https://www.jmir.org/2023/1/e44265 %U https://doi.org/10.2196/44265 %U http://www.ncbi.nlm.nih.gov/pubmed/38109188 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44171 %T An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications %A Zhu,Hongjian %A Wong,Weng Kee %+ Department of Biostatistics, Fielding School of Public Health, University of California at Los Angeles, 605 Charles Young Drive, Los Angeles, CA, 90095, United States, 1 2069622, wkwong@ucla.edu %K doubly adaptive biased coin designs %K model-based optimal designs %K particle swarm optimization %K repair mechanism %D 2023 %7 16.10.2023 %9 Viewpoint %J J Med Internet Res %G English %X Adaptive designs are increasingly developed and used to improve all phases of clinical trials and in biomedical studies in various ways to address different statistical issues. We first present an overview of adaptive designs and note their numerous advantages over traditional clinical trials. In particular, we provide a concrete demonstration that shows how recent adaptive design strategies can further improve an adaptive trial implemented 13 years ago. Despite their usefulness, adaptive designs are still not widely implemented in clinical trials. We offer a few possible reasons and propose some ways to use them more broadly in practice, which include greater availability of software tools and interactive websites to generate optimal adaptive trials freely and effectively, including the use of metaheuristics to facilitate the search for an efficient trial design. To this end, we present several web-based tools for finding various adaptive and nonadaptive optimal designs and discuss nature-inspired metaheuristics. Metaheuristics are assumptions-free general purpose optimization algorithms widely used in computer science and engineering to tackle all kinds of challenging optimization problems, and their use in designing clinical trials is just emerging. We describe a few recent such applications and some of their capabilities for designing various complex trials. Particle swarm optimization is an exemplary nature-inspired algorithm, and similar to others, it has a simple definition but many moving parts, making it hard to study its properties analytically. We investigated one of its hitherto unstudied issues on how to bring back out-of-range candidates during the search for the optimum of the search domain and show that different strategies can impact the success and time of the search. We conclude with a few caveats on the use of metaheuristics for a successful search. %M 37843888 %R 10.2196/44171 %U https://www.jmir.org/2023/1/e44171 %U https://doi.org/10.2196/44171 %U http://www.ncbi.nlm.nih.gov/pubmed/37843888 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47884 %T Ethical Imperatives for Working With Diverse Populations in Digital Research %A Herington,Jonathan %A Connelly,Kay %A Illes,Judy %+ Department of Health Humanities and Bioethics, University of Rochester, Box 676, 601 Elmwood Ave, Rochester, NY, 14642, United States, 1 (585) 275 5800, jonathan.herington@rochester.edu %K digital health research %K justice %K research ethics %K diversity %K engagement %K research participants %K participatory %D 2023 %7 18.9.2023 %9 Viewpoint %J J Med Internet Res %G English %X Digital research methodologies are driving a revolution in health technology but do not yet fully engage diverse and historically underrepresented populations. In this paper, we explore the ethical imperative for such engagement alongside accompanying challenges related to recruitment, appreciation of risk, and confidentiality, among others. We critically analyze existing research ethics frameworks and find that their reliance on individualistic and autonomy-focused models of research ethics does not offer adequate protection in the context of the diversity imperative. To meet the requirements of justice and inclusivity in digital research, methods will benefit from a reorientation toward more participatory practices. %M 37721792 %R 10.2196/47884 %U https://www.jmir.org/2023/1/e47884 %U https://doi.org/10.2196/47884 %U http://www.ncbi.nlm.nih.gov/pubmed/37721792 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 6 %N %P e51776 %T Shaping the Future of Older Adult Care: ChatGPT, Advanced AI, and the Transformation of Clinical Practice %A Fear,Kathleen %A Gleber,Conrad %+ UR Health Lab, University of Rochester Medical Center, 30 Corporate Woods, Suite 180, Rochester, NY, 14623, United States, 1 585 341 4954, kathleen_fear@urmc.rochester.edu %K generative AI %K artificial intelligence %K large language models %K ChatGPT %K Generative Pre-trained Transformer %D 2023 %7 13.9.2023 %9 Guest Editorial %J JMIR Aging %G English %X As the older adult population in the United States grows, new approaches to managing and streamlining clinical work are needed to accommodate their increased demand for health care. Deep learning and generative artificial intelligence (AI) have the potential to transform how care is delivered and how clinicians practice in geriatrics. In this editorial, we explore the opportunities and limitations of these technologies. %M 37703085 %R 10.2196/51776 %U https://aging.jmir.org/2023/1/e51776 %U https://doi.org/10.2196/51776 %U http://www.ncbi.nlm.nih.gov/pubmed/37703085 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47540 %T Sharing Data With Shared Benefits: Artificial Intelligence Perspective %A Tajabadi,Mohammad %A Grabenhenrich,Linus %A Ribeiro,Adèle %A Leyer,Michael %A Heider,Dominik %+ Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, Marburg, 35043, Germany, 49 6421 2821579, dominik.heider@uni-marburg.de %K federated learning %K machine learning %K medical data %K fairness %K data sharing %K artificial intelligence %K development %K artificial intelligence model %K applications %K data analysis %K diagnostic tool %K tool %D 2023 %7 29.8.2023 %9 Viewpoint %J J Med Internet Res %G English %X Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner. %M 37642995 %R 10.2196/47540 %U https://www.jmir.org/2023/1/e47540 %U https://doi.org/10.2196/47540 %U http://www.ncbi.nlm.nih.gov/pubmed/37642995 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e40031 %T Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges %A Chenais,Gabrielle %A Lagarde,Emmanuel %A Gil-Jardiné,Cédric %+ Bordeaux Population Health Center, INSERM U1219, 146 rue Léo Saignat, Bordeaux, 33000, France, 33 05 57 57 15 0, gabrielle.chenais@u-bordeaux.fr %K viewpoint %K ethics %K artificial intelligence %K emergency medicine %K perspectives %K mobile phone %D 2023 %7 23.5.2023 %9 Viewpoint %J J Med Internet Res %G English %X Emergency medicine and its services have reached a breaking point during the COVID-19 pandemic. This pandemic has highlighted the failures of a system that needs to be reconsidered, and novel approaches need to be considered. Artificial intelligence (AI) has matured to the point where it is poised to fundamentally transform health care, and applications within the emergency field are particularly promising. In this viewpoint, we first attempt to depict the landscape of AI-based applications currently in use in the daily emergency field. We review the existing AI systems; their algorithms; and their derivation, validation, and impact studies. We also propose future directions and perspectives. Second, we examine the ethics and risk specificities of the use of AI in the emergency field. %M 36972306 %R 10.2196/40031 %U https://www.jmir.org/2023/1/e40031 %U https://doi.org/10.2196/40031 %U http://www.ncbi.nlm.nih.gov/pubmed/36972306 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e43871 %T One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case %A Benis,Arriel %A Haghi,Mostafa %A Deserno,Thomas M %A Tamburis,Oscar %+ Department of Digital Medical Technologies, Holon Institute of Technology, 52 Golomb Street, Holon, 5810201, Israel, 972 03 5026892, arrielb@hit.ac.il %K One Health %K Digital Health %K One Digital Health %K accident and emergency informatics %K eHealth %K informatics %K medicine %K veterinary medicine %K environmental monitoring %K education %K patient engagement %K citizen science %K data science %K pets %K human-animal bond %K intervention %K ambulatory monitoring %K health monitoring %K Internet of Things %K smart environment %K mobile phone %D 2023 %7 19.5.2023 %9 Viewpoint %J JMIR Med Inform %G English %X Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other’s health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a “how-to” analysis of Tracy and Mego’s daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This “how-to” can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and “how-to's” to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management. %M 36305540 %R 10.2196/43871 %U https://medinform.jmir.org/2023/1/e43871 %U https://doi.org/10.2196/43871 %U http://www.ncbi.nlm.nih.gov/pubmed/36305540 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e44784 %T Integrated Personal Health Record in Indonesia: Design Science Research Study %A Harahap,Nabila Clydea %A Handayani,Putu Wuri %A Hidayanto,Achmad Nizar %+ Faculty of Computer Science, University of Indonesia, Kampus UI Depok, Pondok Cina, Beji, Depok, 16424, Indonesia, 62 8571652699, nabila.clydea@ui.ac.id %K personal health record %K integrated %K Indonesia %K design science %K mobile phone %D 2023 %7 14.3.2023 %9 Original Paper %J JMIR Med Inform %G English %X 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. %M 36917168 %R 10.2196/44784 %U https://medinform.jmir.org/2023/1/e44784 %U https://doi.org/10.2196/44784 %U http://www.ncbi.nlm.nih.gov/pubmed/36917168 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e41212 %T Developing a Capsule Clinic—A 24-Hour Institution for Improving Primary Health Care Accessibility: Evidence From China %A Li,Dongliang %A Zhang,Rujia %A Chen,Chun %A Huang,Yunyun %A Wang,Xiaoyi %A Yang,Qingren %A Zhu,Xuebo %A Zhang,Xiangyang %A Hao,Mo %A Shui,Liming %+ Yinzhou District Health Bureau, No 1221, Bachelor Road, Shounan Street, Yinzhou District, Ningbo, 315199, China, 86 13967820698, 707761065@qq.com %K primary health care %K accessibility %K capsule clinic %K 24-hour clinic %K big-data %K China %K United Nations %K internet clinic %D 2023 %7 9.1.2023 %9 Viewpoint %J JMIR Med Inform %G English %X Telehealth is an effective combination of medical service and intelligent technology. It can improve the problem of remote access to medical care. However, an imbalance in the allocation of health resources still occurs. People spend more time and money to access higher-quality services, which results in inequitable access to primary health care (PHC). At the same time, patients’ usage of telehealth services is limited by the equipment and their own knowledge, and the PHC service suffers from low usage efficiency and lack of service supply. Therefore, improving PHC accessibility is crucial to narrowing the global health care coverage gap and maintaining health equity. In recent years, China has explored several new approaches to improve PHC accessibility. One such approach is the capsule clinic, an emerging institution that represents an upgraded version of the internet hospital. In coordination with the United Nations, the Yinzhou district of Ningbo city in Zhejiang, China, has been testing this new model since 2020. As of October 2022, the number of applications in Ningbo was 15, and the number of users reached 12,219. Unlike internet hospitals, the entire process—from diagnosis to prescription services—can be completed at the capsule clinic. The 24-hour telehealth service could also solve transportation problems and save time for users. Big data analysis can accurately identify regional populations’ PHC service needs and improve efficiency in health resource allocation. The user-friendly, low-cost, and easily accessible telehealth model is of great significance. Installation of capsule clinics would improve PHC accessibility and resolve the uneven distribution of health resources to promote health equity. %M 36622737 %R 10.2196/41212 %U https://medinform.jmir.org/2023/1/e41212 %U https://doi.org/10.2196/41212 %U http://www.ncbi.nlm.nih.gov/pubmed/36622737 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 11 %P e40039 %T Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study %A Alexander,Natasha %A Aftandilian,Catherine %A Guo,Lin Lawrence %A Plenert,Erin %A Posada,Jose %A Fries,Jason %A Fleming,Scott %A Johnson,Alistair %A Shah,Nigam %A Sung,Lillian %+ Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G1X8, Canada, 1 416 813 5287, lillian.sung@sickkids.ca %K machine learning %K clinical utilization %K preferences %K qualitative interviews %D 2022 %7 17.11.2022 %9 Original Paper %J JMIR Med Inform %G English %X 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. %M 36394938 %R 10.2196/40039 %U https://medinform.jmir.org/2022/11/e40039 %U https://doi.org/10.2196/40039 %U http://www.ncbi.nlm.nih.gov/pubmed/36394938 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 11 %P e35138 %T Realizing the Potential of Computer-Assisted Surgery by Embedding Digital Twin Technology %A Qin,Jiaxin %A Wu,Jian %+ Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Building L, 3th Fl., Taoyuan Street, University Town, Shenzhen, 518055, China, 86 13751113096, wuj@sz.tsinghua.edu.cn %K computer-assisted surgery %K digital twin %K virtual space %K surgical navigation %K remote surgery %D 2022 %7 8.11.2022 %9 Viewpoint %J JMIR Med Inform %G English %X The value of virtual world and digital phenotyping has been demonstrated in several fields, and their applications in the field of surgery are worthy of attention and exploration. This viewpoint describes the necessity and approach to understanding the deeper potential of computer-assisted surgery through interaction and symbiosis between virtual and real spaces. We propose to embed digital twin technology into all aspects of computer-assisted surgery rather than just the surgical object and further apply it to the whole process from patient treatment to recovery. A more personalized, precise, and predictable surgery is our vision. %M 36346669 %R 10.2196/35138 %U https://medinform.jmir.org/2022/11/e35138 %U https://doi.org/10.2196/35138 %U http://www.ncbi.nlm.nih.gov/pubmed/36346669 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 10 %P e38557 %T Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities %A Maletzky,Alexander %A Böck,Carl %A Tschoellitsch,Thomas %A Roland,Theresa %A Ludwig,Helga %A Thumfart,Stefan %A Giretzlehner,Michael %A Hochreiter,Sepp %A Meier,Jens %+ Research Department Medical Informatics, RISC Software GmbH, Softwarepark 32a, Hagenberg, 4232, Austria, 43 7236 93028406, alexander.maletzky@risc-software.at %K electronic health record %K medical data preparation %K machine learning %K retrospective data analysis %D 2022 %7 21.10.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital’s data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one’s own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls. %M 36269654 %R 10.2196/38557 %U https://medinform.jmir.org/2022/10/e38557 %U https://doi.org/10.2196/38557 %U http://www.ncbi.nlm.nih.gov/pubmed/36269654 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 9 %P e39746 %T Using Electronic Health Records for the Learning Health System: Creation of a Diabetes Research Registry %A Wells,Brian J %A Downs,Stephen M %A Ostasiewski,Brian %+ Department of Biostatistics and Data Science, Wake Forest University School of Medicine, 1 Medical Center Blvd, Winston Salem, NC, 27157, United States, 1 336 416 5185, bjwells@wakehealth.edu %K electronic health record %K EHR %K Learning Health System %K registry %K diabetes %D 2022 %7 23.9.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Electronic health records (EHRs) were originally developed for clinical care and billing. As such, the data are not collected, organized, and curated in a fashion that is optimized for secondary use to support the Learning Health System. Population health registries provide tools to support quality improvement. These tools are generally integrated with the live EHR, are intended to use a minimum of computing resources, and may not be appropriate for some research projects. Researchers may require different electronic phenotypes and variable definitions from those typically used for population health, and these definitions may vary from study to study. Establishing a formal registry that is mapped to the Observation Medical Outcomes Partnership common data model provides an opportunity to add custom mappings and more easily share these with other institutions. Performing preprocessing tasks such as data cleaning, calculation of risk scores, time-to-event analysis, imputation, and transforming data into a format for statistical analyses will improve efficiency and make the data easier to use for investigators. Research registries that are maintained outside the EHR also have the luxury of using significant computational resources without jeopardizing clinical care data. This paper describes a virtual Diabetes Registry at Atrium Health Wake Forest Baptist and the plan for its continued development. %M 36149742 %R 10.2196/39746 %U https://medinform.jmir.org/2022/9/e39746 %U https://doi.org/10.2196/39746 %U http://www.ncbi.nlm.nih.gov/pubmed/36149742 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 8 %P e34304 %T Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint %A Hu,Zoe %A Hu,Ricky %A Yau,Olivia %A Teng,Minnie %A Wang,Patrick %A Hu,Grace %A Singla,Rohit %+ School of Medicine, Queen's University, 166 Brock Street, Kingston, ON, K7L5G2, Canada, 1 6132042952, zhu@qmed.ca %K medical education %K artificial intelligence %K health care trainees %K AI %K health care workers %D 2022 %7 15.8.2022 %9 Viewpoint %J JMIR Med Inform %G English %X The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future. %M 35969464 %R 10.2196/34304 %U https://medinform.jmir.org/2022/8/e34304 %U https://doi.org/10.2196/34304 %U http://www.ncbi.nlm.nih.gov/pubmed/35969464 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 8 %P e37756 %T Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward %A Krzyzanowski,Brittany %A Manson,Steven M %+ University of Minnesota, 269 19th Avenue South, Minneapolis, MN, 55455, United States, 1 612 625 5000, krzyz016@umn.edu %K Health Insurance Portability and Accountability Act %K HIPAA %K data privacy %K health %K maps %K safe harbor %K visualization %K patient privacy %D 2022 %7 3.8.2022 %9 Viewpoint %J JMIR Med Inform %G English %X The Health Insurance Portability and Accountability Act (HIPAA) was an important milestone in protecting the privacy of patient data; however, the HIPAA provisions specific to geographic data remain vague and hinder the ways in which epidemiologists and geographers use and share spatial health data. The literature on spatial health and select legal and official guidance documents present scholars with ambiguous guidelines that have led to the use and propagation of multiple interpretations of a single HIPAA safe harbor provision specific to geographic data. Misinterpretation of this standard has resulted in many entities sharing data at overly conservative levels, whereas others offer definitions of safe harbors that potentially put patient data at risk. To promote understanding of, and adherence to, the safe harbor rule, this paper reviews the HIPAA law from its creation to the present day, elucidating common misconceptions and presenting straightforward guidance to scholars. We focus on the 20,000-person population threshold and the 3-digit zip code stipulation of safe harbors, which are central to the confusion surrounding how patient location data can be shared. A comprehensive examination of these 2 stipulations, which integrates various expert perspectives and relevant studies, reveals how alternative methods for safe harbors can offer researchers better data and better data protection. Much has changed in the 20 years since the introduction of the safe harbor provision; however, it continues to be the primary source of guidance (and frustration) for researchers trying to share maps, leaving many waiting for these rules to be revised in accordance with the times. %M 35921140 %R 10.2196/37756 %U https://medinform.jmir.org/2022/8/e37756 %U https://doi.org/10.2196/37756 %U http://www.ncbi.nlm.nih.gov/pubmed/35921140 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 7 %P e39145 %T The Power of Patient Engagement With Electronic Health Records as Research Participants %A Pawelek,Jeff %A Baca-Motes,Katie %A Pandit,Jay A %A Berk,Benjamin B %A Ramos,Edward %+ Digital Trials Center, Scripps Research Translational Institute, 3344 N Torrey Pines Ct, Plaza Level, La Jolla, CA, 92037, United States, 1 858 784 2028, eramos@scripps.edu %K electronic health record %K EHR %K digital health technology %K digital clinical trial %K underrepresentation %K underrepresented in biomedical research %K biomedical research %D 2022 %7 8.7.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants. %M 35802410 %R 10.2196/39145 %U https://medinform.jmir.org/2022/7/e39145 %U https://doi.org/10.2196/39145 %U http://www.ncbi.nlm.nih.gov/pubmed/35802410 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 6 %P e34204 %T Quality Criteria for Real-world Data in Pharmaceutical Research and Health Care Decision-making: Austrian Expert Consensus %A Klimek,Peter %A Baltic,Dejan %A Brunner,Martin %A Degelsegger-Marquez,Alexander %A Garhöfer,Gerhard %A Gouya-Lechner,Ghazaleh %A Herzog,Arnold %A Jilma,Bernd %A Kähler,Stefan %A Mikl,Veronika %A Mraz,Bernhard %A Ostermann,Herwig %A Röhl,Claas %A Scharinger,Robert %A Stamm,Tanja %A Strassnig,Michael %A Wirthumer-Hoche,Christa %A Pleiner-Duxneuner,Johannes %+ Gesellschaft für Pharmazeutische Medizin, Engelhorngasse 3, Vienna, 1210, Austria, 43 1 40160 ext 36255, johannes.pleiner-duxneuner@roche.com %K real-world data %K real-world evidence %K data quality %K data quality criteria %K RWD quality recommendations %K pharmaceutical research %K health care decision-making %K quality criteria for RWD in health care %K Gesellschaft für Pharmazeutische Medizin %K GPMed %D 2022 %7 17.6.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Real-world data (RWD) collected in routine health care processes and transformed to real-world evidence have become increasingly interesting within the research and medical communities to enhance medical research and support regulatory decision-making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which qualities RWD must meet in order to be acceptable for decision-making within regulatory or routine clinical decision support. In the absence of guidelines defining the quality standards for RWD, an overview and first recommendations for quality criteria for RWD in pharmaceutical research and health care decision-making is needed in Austria. An Austrian multistakeholder expert group led by Gesellschaft für Pharmazeutische Medizin (Austrian Society for Pharmaceutical Medicine) met regularly; reviewed and discussed guidelines, frameworks, use cases, or viewpoints; and agreed unanimously on a set of quality criteria for RWD. This consensus statement was derived from the quality criteria for RWD to be used more effectively for medical research purposes beyond the registry-based studies discussed in the European Medicines Agency guideline for registry-based studies. This paper summarizes the recommendations for the quality criteria of RWD, which represents a minimum set of requirements. In order to future-proof registry-based studies, RWD should follow high-quality standards and be subjected to the quality assurance measures needed to underpin data quality. Furthermore, specific RWD quality aspects for individual use cases (eg, medical or pharmacoeconomic research), market authorization processes, or postmarket authorization phases have yet to be elaborated. %M 35713954 %R 10.2196/34204 %U https://medinform.jmir.org/2022/6/e34204 %U https://doi.org/10.2196/34204 %U http://www.ncbi.nlm.nih.gov/pubmed/35713954 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 3 %P e32245 %T Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case %A Zirikly,Ayah %A Desmet,Bart %A Newman-Griffis,Denis %A Marfeo,Elizabeth E %A McDonough,Christine %A Goldman,Howard %A Chan,Leighton %+ Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892, United States, 1 301 827 6558, ayah.zirikly@nih.gov %K natural language processing %K text mining %K bioinformatics %K health informatics %K machine learning %K disability %K mental health %K functioning %K NLP %K electronic health record %K framework %K disability %K EHR %K automation %K eHealth %K decision support %K functional status %K whole-person function %D 2022 %7 18.3.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Natural language processing (NLP) in health care enables transformation of complex narrative information into high value products such as clinical decision support and adverse event monitoring in real time via the electronic health record (EHR). However, information technologies for mental health have consistently lagged because of the complexity of measuring and modeling mental health and illness. The use of NLP to support management of mental health conditions is a viable topic that has not been explored in depth. This paper provides a framework for the advanced application of NLP methods to identify, extract, and organize information on mental health and functioning to inform the decision-making process applied to assessing mental health. We present a use-case related to work disability, guided by the disability determination process of the US Social Security Administration (SSA). From this perspective, the following questions must be addressed about each problem that leads to a disability benefits claim: When did the problem occur and how long has it existed? How severe is it? Does it affect the person’s ability to work? and What is the source of the evidence about the problem? Our framework includes 4 dimensions of medical information that are central to assessing disability—temporal sequence and duration, severity, context, and information source. We describe key aspects of each dimension and promising approaches for application in mental functioning. For example, to address temporality, a complete functional timeline must be created with all relevant aspects of functioning such as intermittence, persistence, and recurrence. Severity of mental health symptoms can be successfully identified and extracted on a 4-level ordinal scale from absent to severe. Some NLP work has been reported on the extraction of context for specific cases of wheelchair use in clinical settings. We discuss the links between the task of information source assessment and work on source attribution, coreference resolution, event extraction, and rule-based methods. Gaps were identified in NLP applications that directly applied to the framework and in existing relevant annotated data sets. We highlighted NLP methods with the potential for advanced application in the field of mental functioning. Findings of this work will inform the development of instruments for supporting SSA adjudicators in their disability determination process. The 4 dimensions of medical information may have relevance for a broad array of individuals and organizations responsible for assessing mental health function and ability. Further, our framework with 4 specific dimensions presents significant opportunity for the application of NLP in the realm of mental health and functioning beyond the SSA setting, and it may support the development of robust tools and methods for decision-making related to clinical care, program implementation, and other outcomes. %M 35302510 %R 10.2196/32245 %U https://medinform.jmir.org/2022/3/e32245 %U https://doi.org/10.2196/32245 %U http://www.ncbi.nlm.nih.gov/pubmed/35302510 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 3 %P e27691 %T Primary Care: The Actual Intelligence Required for Artificial Intelligence to Advance Health Care and Improve Health %A Liaw,Winston R %A Westfall,John M %A Williamson,Tyler S %A Jabbarpour,Yalda %A Bazemore,Andrew %+ Department of Health Systems and Population Health Sciences, University of Houston, 4349 Martin Luther King Blvd, Houston, TX, 77204, United States, 1 7137439862, winstonrliaw@gmail.com %K artificial intelligence %K primary care %D 2022 %7 8.3.2022 %9 Viewpoint %J JMIR Med Inform %G English %X With conversational agents triaging symptoms, cameras aiding diagnoses, and remote sensors monitoring vital signs, the use of artificial intelligence (AI) outside of hospitals has the potential to improve health, according to a recently released report from the National Academy of Medicine. Despite this promise, the success of AI is not guaranteed, and stakeholders need to be involved with its development to ensure that the resulting tools can be easily used by clinicians, protect patient privacy, and enhance the value of the care delivered. A crucial stakeholder group missing from the conversation is primary care. As the nation’s largest delivery platform, primary care will have a powerful impact on whether AI is adopted and subsequently exacerbates health disparities. To leverage these benefits, primary care needs to serve as a medical home for AI, broaden its teams and training, and build on government initiatives and funding. %M 35258464 %R 10.2196/27691 %U https://medinform.jmir.org/2022/3/e27691 %U https://doi.org/10.2196/27691 %U http://www.ncbi.nlm.nih.gov/pubmed/35258464 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 3 %P e33044 %T A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma %A Luo,Gang %+ Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047, Seattle, WA, 98195, United States, 1 206 221 4596, gangluo@cs.wisc.edu %K clinical decision support %K forecasting %K machine learning %K patient care management %K medical informatics %K asthma %K health care %K health care systems %K health care costs %K prediction models %K risk prediction %D 2022 %7 1.3.2022 %9 Viewpoint %J JMIR Med Inform %G English %X In the United States, ~9% of people have asthma. Each year, asthma incurs high health care cost and many hospital encounters covering 1.8 million emergency room visits and 439,000 hospitalizations. A small percentage of patients with asthma use most health care resources. To improve outcomes and cut resource use, many health care systems use predictive models to prospectively find high-risk patients and enroll them in care management for preventive care. For maximal benefit from costly care management with limited service capacity, only patients at the highest risk should be enrolled. However, prior models built by others miss >50% of true highest-risk patients and mislabel many low-risk patients as high risk, leading to suboptimal care and wasted resources. To address this issue, 3 site-specific models were recently built to predict hospital encounters for asthma, gaining up to >11% better performance. However, these models do not generalize well across sites and patient subgroups, creating 2 gaps before translating these models into clinical use. This paper points out these 2 gaps and outlines 2 corresponding solutions: (1) a new machine learning technique to create cross-site generalizable predictive models to accurately find high-risk patients and (2) a new machine learning technique to automatically raise model performance for poorly performing subgroups while maintaining model performance on other subgroups. This gives a roadmap for future research. %M 35230246 %R 10.2196/33044 %U https://medinform.jmir.org/2022/3/e33044 %U https://doi.org/10.2196/33044 %U http://www.ncbi.nlm.nih.gov/pubmed/35230246 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 2 %P e33848 %T A Free, Open-Source, Offline Digital Health System for Refugee Care %A Ashworth,Henry %A Ebrahim,Senan %A Ebrahim,Hassaan %A Bhaiwala,Zahra %A Chilazi,Michael %+ Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, United States, 1 8052152433, hcashwor@gmail.com %K electronic health record %K mHealth %K refugee %K displaced population %K digital health %K COVID-19 %K health care %D 2022 %7 11.2.2022 %9 Viewpoint %J JMIR Med Inform %G English %X 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. %M 35147509 %R 10.2196/33848 %U https://medinform.jmir.org/2022/2/e33848 %U https://doi.org/10.2196/33848 %U http://www.ncbi.nlm.nih.gov/pubmed/35147509 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 2 %P e32875 %T 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 %A Sezgin,Emre %A Sirrianni,Joseph %A Linwood,Simon L %+ The Abigail Wexner Research Institute, Nationwide Children's Hospital, 700 Children's Drive, Columbus, OH, 43205, United States, 1 6143556814, esezgin1@gmail.com %K natural language processing %K artificial intelligence %K generative pretrained transformer %K clinical informatics %K chatbot %D 2022 %7 10.2.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Generative pretrained transformer models have been popular recently due to their enhanced capabilities and performance. In contrast to many existing artificial intelligence models, generative pretrained transformer models can perform with very limited training data. Generative pretrained transformer 3 (GPT-3) is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts. Some examples include writing essays, answering complex questions, matching pronouns to their nouns, and conducting sentiment analyses. However, questions remain with regard to its implementation in health care, specifically in terms of operationalization and its use in clinical practice and research. In this viewpoint paper, we briefly introduce GPT-3 and its capabilities and outline considerations for its implementation and operationalization in clinical practice through a use case. The implementation considerations include (1) processing needs and information systems infrastructure, (2) operating costs, (3) model biases, and (4) evaluation metrics. In addition, we outline the following three major operational factors that drive the adoption of GPT-3 in the US health care system: (1) ensuring Health Insurance Portability and Accountability Act compliance, (2) building trust with health care providers, and (3) establishing broader access to the GPT-3 tools. This viewpoint can inform health care practitioners, developers, clinicians, and decision makers toward understanding the use of the powerful artificial intelligence tools integrated into hospital systems and health care. %M 35142635 %R 10.2196/32875 %U https://medinform.jmir.org/2022/2/e32875 %U https://doi.org/10.2196/32875 %U http://www.ncbi.nlm.nih.gov/pubmed/35142635 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e34038 %T Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device %A Carolan,Jane Elizabeth %A McGonigle,John %A Dennis,Andrea %A Lorgelly,Paula %A Banerjee,Amitava %+ Institute of Health Informatics, University College London, Gower Street, London, WC1E 6BT, United Kingdom, 44 07464345635, j.carolan@ucl.ac.uk %K Artificial intelligence %K machine learning %K algorithm %K software %K risk assessment %K informatics %D 2022 %7 27.1.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Artificial intelligence (AI) is a broad discipline that aims to understand and design systems that display properties of intelligence. Machine learning (ML) is a subset of AI that describes how algorithms and models can assist computer systems in progressively improving their performance. In health care, an increasingly common application of AI/ML is software as a medical device (SaMD), which has the intention to diagnose, treat, cure, mitigate, or prevent disease. AI/ML includes either “locked” or “continuous learning” algorithms. Locked algorithms consistently provide the same output for a particular input. Conversely, continuous learning algorithms, in their infancy in terms of SaMD, modify in real-time based on incoming real-world data, without controlled software version releases. This continuous learning has the potential to better handle local population characteristics, but with the risk of reinforcing existing structural biases. Continuous learning algorithms pose the greatest regulatory complexity, requiring seemingly continuous oversight in the form of special controls to ensure ongoing safety and effectiveness. We describe the challenges of continuous learning algorithms, then highlight the new evidence standards and frameworks under development, and discuss the need for stakeholder engagement. The paper concludes with 2 key steps that regulators need to address in order to optimize and realize the benefits of SaMD: first, international standards and guiding principles addressing the uniqueness of SaMD with a continuous learning algorithm are required and second, throughout the product life cycle and appropriate to the SaMD risk classification, there needs to be continuous communication between regulators, developers, and SaMD end users to ensure vigilance and an accurate understanding of the technology. %M 35084352 %R 10.2196/34038 %U https://medinform.jmir.org/2022/1/e34038 %U https://doi.org/10.2196/34038 %U http://www.ncbi.nlm.nih.gov/pubmed/35084352 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e31837 %T Impact of Electronic Health Record Interoperability on Telehealth Service Outcomes %A Zhang,Xinyue %A Saltman,Richard %+ Department of Health Policy and Management, Rollins School of Public Health, Emory University, 1518 Clifton Rd, 6th Floor, Atlanta, GA, 30322, United States, 1 2068494755, xinyue.zhang2@emory.edu %K Electronic Health Records %K Telehealth %K Telemental health %K Pandemic %K Health outcomes %K Health Policy %D 2022 %7 11.1.2022 %9 Viewpoint %J JMIR Med Inform %G English %X This paper aims to develop a telehealth success model and discusses three critical components: (1) health information quality, (2) electronic health record system quality, and (3) telehealth service quality to ensure effective telehealth service delivery, reduce professional burnout, and enhance access to care. The paper applied a policy analysis method and discussed telehealth applications in rural health, mental health, and veterans health services. The results pointed out the fact that, although telehealth paired with semantic/organizational interoperability facilitates value-based and team-based care, challenges remain to enhance user (both patients and clinicians) experience and satisfaction. The conclusion indicates that approaches at systemic and physician levels are needed to reduce disparities in health technology adoption and improve access to telehealth care. %M 34890347 %R 10.2196/31837 %U https://medinform.jmir.org/2022/1/e31837 %U https://doi.org/10.2196/31837 %U http://www.ncbi.nlm.nih.gov/pubmed/34890347 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 12 %P e25328 %T Can Real-time Computer-Aided Detection Systems Diminish the Risk of Postcolonoscopy Colorectal Cancer? %A Madalinski,Mariusz %A Prudham,Roger %+ Northern Care Alliance, Royal Oldham Hospital, Rochdale Rd, Oldham, OL1 2JH, United Kingdom, 44 01616240420, mariusz.madalinski@googlemail.com %K artificial intelligence %K colonoscopy %K adenoma %K real-time computer-aided detection %K colonic polyp %D 2021 %7 24.12.2021 %9 Viewpoint %J JMIR Med Inform %G English %X The adenoma detection rate is the constant subject of research and the main marker of quality in bowel cancer screening. However, by improving the quality of endoscopy via artificial intelligence methods, all polyps, including those with the potential for malignancy, can be removed, thereby reducing interval colorectal cancer rates. As such, the removal of all polyps may become the best marker of endoscopy quality. Thus, we present a viewpoint on integrating the computer-aided detection (CADe) of polyps with high-accuracy, real-time colonoscopy to challenge quality improvements in the performance of colonoscopy. Colonoscopy for bowel cancer screening involving the integration of a deep learning methodology (ie, integrating artificial intelligence with CADe systems) has been assessed in an effort to increase the adenoma detection rate. In this viewpoint, a few studies are described, and their results show that CADe systems are able to increase screening sensitivity. The detection of adenomatous polyps, which are associated with a potential risk of progression to colorectal cancer, and their removal are expected to reduce cancer incidence and mortality rates. However, so far, artificial intelligence methods do not increase the detection of cancer or large adenomatous polyps but contribute to the detection of small precancerous polyps. %M 34571490 %R 10.2196/25328 %U https://medinform.jmir.org/2021/12/e25328 %U https://doi.org/10.2196/25328 %U http://www.ncbi.nlm.nih.gov/pubmed/34571490 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 11 %P e31527 %T The Role of Physicians in Digitalizing Health Care Provision: Web-Based Survey Study %A Burmann,Anja %A Tischler,Max %A Faßbach,Mira %A Schneitler,Sophie %A Meister,Sven %+ Fraunhofer Institute for Software and Systems Engineering, Emil-Figge-Str 91, Dortmund, 44227, Germany, 49 2319 7677435, anja.burmann@isst.fraunhofer.de %K digitalization %K digital transformation %K health care %K human factor %K physicians %K digital natives %K web-based survey %K digital health %D 2021 %7 11.11.2021 %9 Original Paper %J JMIR Med Inform %G English %X 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 (η=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. %M 34545813 %R 10.2196/31527 %U https://medinform.jmir.org/2021/11/e31527 %U https://doi.org/10.2196/31527 %U http://www.ncbi.nlm.nih.gov/pubmed/34545813 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e20046 %T Views on Using Social Robots in Professional Caregiving: Content Analysis of a Scenario Method Workshop %A Busse,Theresa Sophie %A Kernebeck,Sven %A Nef,Larissa %A Rebacz,Patrick %A Kickbusch,Ilona %A Ehlers,Jan Peter %+ Department of Didactics and Educational Research in Healthcare, Faculty of Health, Witten/Herdecke University, Alfred-Herrhausen-Straße 50, Witten, 58448, Germany, 49 2302 926 786 20, theresa.busse@uni-wh.de %K social robots %K robotics %K health care sector %K health personnel %K ethics %K forecasting %K trends %K technology %K digital transformation %K professional caregiving %D 2021 %7 10.11.2021 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 34757318 %R 10.2196/20046 %U https://www.jmir.org/2021/11/e20046 %U https://doi.org/10.2196/20046 %U http://www.ncbi.nlm.nih.gov/pubmed/34757318 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e30605 %T A Program to Improve Digital Access and Literacy Among Community Stakeholders: Cohort Study %A Drazich,Brittany F %A Nyikadzino,Yeukai %A Gleason,Kelly T %+ School of Nursing, Johns Hopkins University, 525 N Wolfe St, Baltimore, MD, 21205, United States, 1 3023530657, bdrazich@umaryland.edu %K technology %K disparities %K digital access %K digital literacy %K community %K stakeholders %K digital health %K digital divide %K patient-centered outcomes %D 2021 %7 10.11.2021 %9 Early Report %J JMIR Form Res %G English %X 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. %M 34757316 %R 10.2196/30605 %U https://formative.jmir.org/2021/11/e30605 %U https://doi.org/10.2196/30605 %U http://www.ncbi.nlm.nih.gov/pubmed/34757316 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 7 %N 4 %P e30873 %T Digital Health and Digital Learning Experiences Across Speech-Language Pathology, Phoniatrics, and Otolaryngology: Interdisciplinary Survey Study %A Lin,Yuchen %A Lemos,Martin %A Neuschaefer-Rube,Christiane %+ Clinic of Phoniatrics, Pedaudiology & Communication Disorders, Medical Faculty, University Hospital Rheinisch-Westfaelische Technische Hochschule Aachen, Pauwelsstraße 30, Aachen, 52074, Germany, 49 0241 80 88954, yuchen.lin@rwth-aachen.de %K digital learning %K e-learning %K speech-language pathology %K phoniatrics %K otolaryngology %K communication disorders %K mobile phone %D 2021 %7 5.11.2021 %9 Original Paper %J JMIR Med Educ %G English %X 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: Logopäd*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. %M 34738911 %R 10.2196/30873 %U https://mededu.jmir.org/2021/4/e30873 %U https://doi.org/10.2196/30873 %U http://www.ncbi.nlm.nih.gov/pubmed/34738911 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 10 %P e30768 %T Moderators of the Effect of a Self-directed Digitally Delivered Exercise Program for People With Knee Osteoarthritis: Exploratory Analysis of a Randomized Controlled Trial %A Nelligan,Rachel K %A Hinman,Rana S %A McManus,Fiona %A Lamb,Karen E %A Bennell,Kim L %+ Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, School of Health Sciences, The University of Melbourne, 161 Barry Street, Parkville, 3010, Australia, 61 403652115, rachel.nelligan@unimelb.edu.au %K digital %K text messaging %K exercise %K moderators %K osteoarthritis %K RCT %K clinical trial %K subgroups %K pain %K function %K knee osteoarthritis %K rehabilitation %K digital health %D 2021 %7 29.10.2021 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 34714252 %R 10.2196/30768 %U https://www.jmir.org/2021/10/e30768 %U https://doi.org/10.2196/30768 %U http://www.ncbi.nlm.nih.gov/pubmed/34714252 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 9 %P e18471 %T Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts %A Sarker,Abeed %A Al-Garadi,Mohammed Ali %A Yang,Yuan-Chi %A Choi,Jinho %A Quyyumi,Arshed A %A Martin,Greg S %+ Department of Biomedical Informatics, School of Medicine, Emory University, 101 Woodruff Circle, Office 4101, Atlanta, GA, 30322, United States, 1 404 712 0055, abeed@dbmi.emory.edu %K natural language processing %K text mining %K patient-centered care %K evidence-based medicine %K medical informatics %D 2021 %7 28.9.2021 %9 Viewpoint %J JMIR Med Inform %G English %X The capabilities of natural language processing (NLP) methods have expanded significantly in recent years, and progress has been particularly driven by advances in data science and machine learning. However, NLP is still largely underused in patient-oriented clinical research and care (POCRC). A key reason behind this is that clinical NLP methods are typically developed, optimized, and evaluated with narrowly focused data sets and tasks (eg, those for the detection of specific symptoms in free texts). Such research and development (R&D) approaches may be described as problem oriented, and the developed systems perform specialized tasks well. As standalone systems, however, they generally do not comprehensively meet the needs of POCRC. Thus, there is often a gap between the capabilities of clinical NLP methods and the needs of patient-facing medical experts. We believe that to increase the practical use of biomedical NLP, future R&D efforts need to be broadened to a new research paradigm—one that explicitly incorporates characteristics that are crucial for POCRC. We present our viewpoint about 4 such interrelated characteristics that can increase NLP systems’ suitability for POCRC (3 that represent NLP system properties and 1 associated with the R&D process)—(1) interpretability (the ability to explain system decisions), (2) patient centeredness (the capability to characterize diverse patients), (3) customizability (the flexibility for adapting to distinct settings, problems, and cohorts), and (4) multitask evaluation (the validation of system performance based on multiple tasks involving heterogeneous data sets). By using the NLP task of clinical concept detection as an example, we detail these characteristics and discuss how they may result in the increased uptake of NLP systems for POCRC. %M 34581670 %R 10.2196/18471 %U https://medinform.jmir.org/2021/9/e18471 %U https://doi.org/10.2196/18471 %U http://www.ncbi.nlm.nih.gov/pubmed/34581670 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 9 %P e29374 %T Using a New Model of Electronic Health Record Training to Reduce Physician Burnout: A Plan for Action %A Mohan,Vishnu %A Garrison,Cort %A Gold,Jeffrey A %+ Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Mail Code BICC, Portland, OR, 97239-3098, United States, 1 5034944469, mohanv@ohsu.edu %K electronic health records %K clinician burnout %K EHR training %K clinician wellness %K after-hours EHR use %K EHR %K patient data %K burnout %K simulation %K efficiency %K optimization %K well-being %D 2021 %7 20.9.2021 %9 Viewpoint %J JMIR Med Inform %G English %X Physician burnout in the United States has been growing at an alarming rate, and health care organizations are beginning to invest significant resources in combating this phenomenon. Although the causes for burnout are multifactorial, a key issue that affects physicians is that they spend a significant proportion of their time interacting with their electronic health record (EHR) system, primarily because of the need to sift through increasing amounts of patient data, coupled with a significant documentation burden. This has led to physicians spending increasing amounts of time with the EHR outside working hours trying to catch up on paperwork (“pajama time”), which is a factor linked to burnout. In this paper, we propose an innovative model of EHR training using high-fidelity EHR simulations designed to facilitate efficient optimization of EHR use by clinicians and emphasize the importance of both lifelong learning and physician well-being. %M 34325400 %R 10.2196/29374 %U https://medinform.jmir.org/2021/9/e29374 %U https://doi.org/10.2196/29374 %U http://www.ncbi.nlm.nih.gov/pubmed/34325400 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 9 %P e28776 %T Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice %A Kulkarni,Viraj %A Gawali,Manish %A Kharat,Amit %+ DeepTek Inc, 2rd Floor, Alacrity Innovation Centre, 3, Baner Rd, Pallod Farms, Baner, Pune, 411045, India, 91 72760 60080, manish.gawali@deeptek.ai %K artificial intelligence %K AI %K machine learning %K deep learning %K radiology %K privacy %K neural networks %K deployment %D 2021 %7 9.9.2021 %9 Viewpoint %J JMIR Med Inform %G English %X The use of machine learning to develop intelligent software tools for the interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. We discuss insufficient training data, decentralized data sets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen data sets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify the techniques used to address it. Although these techniques have been discussed in prior research, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders. %M 34499049 %R 10.2196/28776 %U https://medinform.jmir.org/2021/9/e28776 %U https://doi.org/10.2196/28776 %U http://www.ncbi.nlm.nih.gov/pubmed/34499049 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 8 %P e23219 %T A System to Support Diverse Social Program Management %A McKillop,Mollie %A Snowdon,Jane %A Willis,Van C %A Alevy,Shira %A Rizvi,Rubina %A Rewalt,Karen %A Lefebvre-Paillé,Charlyne %A Kassler,William %A Purcell Jackson,Gretchen %+ IBM Watson Health, 75 Binney Street, Cambridge, MA, 02142, United States, 1 3322073519, mollie.mckillop@ibm.com %K other clinical informatics applications %K process management tools %K requirements analysis and design %K consumer health informatics %K public health %D 2021 %7 30.8.2021 %9 Viewpoint %J JMIR Med Inform %G English %X 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. %M 34459741 %R 10.2196/23219 %U https://medinform.jmir.org/2021/8/e23219 %U https://doi.org/10.2196/23219 %U http://www.ncbi.nlm.nih.gov/pubmed/34459741 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 8 %P e16293 %T Potential Uses of Blockchain Technology for Outcomes Research on Opioids %A Gonzales,Aldren %A Smith,Scott R %A Dullabh,Prashila %A Hovey,Lauren %A Heaney-Huls,Krysta %A Robichaud,Meagan %A Boodoo,Roger %+ US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Office of Health Policy, 200 Independence Ave SW, Washington, DC, 20201, United States, 1 2028707414, aldren.gonzales@hhs.gov %K blockchain %K distributed ledger %K opioid crisis %K outcomes research %K patient-centered outcomes research %K mobile phone %D 2021 %7 27.8.2021 %9 Viewpoint %J JMIR Med Inform %G English %X The scale and severity of the opioid epidemic call for innovative, multipronged solutions. Research and development is key to accelerate the discovery and evaluation of interventions that support pain and substance use disorder management. In parallel, the use and integration of blockchain technology within research networks holds the potential to address some of the unique challenges facing opioid research. This paper discusses the applications of blockchain technology and illustrates potential ways in which it could be applied to strengthen the validity of outcomes research on the opioid epidemic. We reviewed published and gray literature to identify useful applications of blockchain, specifically those that address the challenges faced by opioid research networks and programs. We then convened a panel of experts to discuss the strengths, limitations, and feasibility of each application. Blockchain has the potential to address some of the issues surrounding health data management, including data availability, data sharing and interoperability, and privacy and security. We identified five primary applications of blockchain to opioids: clinical trials and pharmaceutical research, incentivizing data donation and behavior change, secure exchange and management of e-prescriptions, supply chain management, and secondary use of clinical data for research and public health surveillance. The published literature was limited, leading us to rely on gray literature, which was also limited in its discussion of the technical aspects of implementation. The technical expert panel provided additional context and an assessment of feasibility that was lacking in the literature. Research on opioid use and misuse is challenging because of disparate data stored across different systems, data and system interoperability issues, and legal requirements. These areas must be navigated to make data accessible, timely, and useful to researchers. Blockchain technologies have the potential to act as a facilitator in this process, offering a more efficient, secure, and privacy-preserving solution for data exchange. Among the 5 primary applications, we found that clinical trial research, supply chain management, and secondary use of data had the most examples in practice and the potential effectiveness of blockchain. More discussions and studies should focus on addressing technical questions concerning scalability and tackling practical concerns such as cost, standards, and governance around the implementation of blockchain in health care. Policy concerns related to balancing the need for data accessibility that also protects patient privacy and autonomy in revoking consent should also be examined. %M 34448721 %R 10.2196/16293 %U https://medinform.jmir.org/2021/8/e16293 %U https://doi.org/10.2196/16293 %U http://www.ncbi.nlm.nih.gov/pubmed/34448721 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 7 %P e27449 %T Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation %A Hogan,Katie %A Macedo,Briana %A Macha,Venkata %A Barman,Arko %A Jiang,Xiaoqian %+ School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin St #600, Houston, TX, 77030, United States, 1 7135003930, xiaoqian.jiang@uth.tmc.edu %K contact tracing %K COVID-19 %K privacy %K smartphone apps %K mobile phone apps %K health information %K electronic health %K eHealth %K pandemic %K app %K mobile health %K mHealth %D 2021 %7 19.7.2021 %9 Viewpoint %J JMIR Med Inform %G English %X The global and national response to the COVID-19 pandemic has been inadequate due to a collective lack of preparation and a shortage of available tools for responding to a large-scale pandemic. By applying lessons learned to create better preventative methods and speedier interventions, the harm of a future pandemic may be dramatically reduced. One potential measure is the widespread use of contact tracing apps. While such apps were designed to combat the COVID-19 pandemic, the time scale in which these apps were deployed proved a significant barrier to efficacy. Many companies and governments sprinted to deploy contact tracing apps that were not properly vetted for performance, privacy, or security issues. The hasty development of incomplete contact tracing apps undermined public trust and negatively influenced perceptions of app efficacy. As a result, many of these apps had poor voluntary public uptake, which greatly decreased the apps’ efficacy. Now, with lessons learned from this pandemic, groups can better design and test apps in preparation for the future. In this viewpoint, we outline common strategies employed for contact tracing apps, detail the successes and shortcomings of several prominent apps, and describe lessons learned that may be used to shape effective contact tracing apps for the present and future. Future app designers can keep these lessons in mind to create a version that is suitable for their local culture, especially with regard to local attitudes toward privacy-utility tradeoffs during public health crises. %M 34254937 %R 10.2196/27449 %U https://medinform.jmir.org/2021/7/e27449 %U https://doi.org/10.2196/27449 %U http://www.ncbi.nlm.nih.gov/pubmed/34254937 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 6 %P e28921 %T Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans %A Makridis,Christos %A Hurley,Seth %A Klote,Mary %A Alterovitz,Gil %+ National Artificial Intelligence Institute, Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC, 20420, United States, 1 2022977787, christos.makridis@va.gov %K artificial intelligence %K ethics %K veterans %K health data %K technology %K Veterans Affairs %K health technology %K data %D 2021 %7 2.6.2021 %9 Viewpoint %J JMIR Med Inform %G English %X 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. %M 34076584 %R 10.2196/28921 %U https://medinform.jmir.org/2021/6/e28921 %U https://doi.org/10.2196/28921 %U http://www.ncbi.nlm.nih.gov/pubmed/34076584 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 5 %P e27778 %T A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support %A Luo,Gang %+ Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047, Seattle, WA, 98195, United States, 1 206 221 4596, gangluo@cs.wisc.edu %K clinical decision support %K database management systems %K forecasting %K machine learning %K electronic medical records %D 2021 %7 27.5.2021 %9 Viewpoint %J JMIR Med Inform %G English %X Using machine learning predictive models for clinical decision support has great potential in improving patient outcomes and reducing health care costs. However, most machine learning models are black boxes that do not explain their predictions, thereby forming a barrier to clinical adoption. To overcome this barrier, an automated method was recently developed to provide rule-style explanations of any machine learning model’s predictions on tabular data and to suggest customized interventions. Each explanation delineates the association between a feature value pattern and an outcome value. Although the association and intervention information is useful, the user of the automated explaining function often requires more detailed information to better understand the patient’s situation and to aid in decision making. More specifically, consider a feature value in the explanation that is computed by an aggregation function on the raw data, such as the number of emergency department visits related to asthma that the patient had in the prior 12 months. The user often wants to rapidly drill through to see certain parts of the related raw data that produce the feature value. This task is frequently difficult and time-consuming because the few pieces of related raw data are submerged by many pieces of raw data of the patient that are unrelated to the feature value. To address this issue, this paper outlines an automated lineage tracing approach, which adds automated drill-through capability to the automated explaining function, and provides a roadmap for future research. %M 34042600 %R 10.2196/27778 %U https://medinform.jmir.org/2021/5/e27778 %U https://doi.org/10.2196/27778 %U http://www.ncbi.nlm.nih.gov/pubmed/34042600 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 11 %P e21874 %T Assessment of mHealth Interventions: Need for New Studies, Methods, and Guidelines for Study Designs %A Ologeanu-Taddei,Roxana %+ TBS Business School, 1, place Alphonse Jourdain, Toulouse, 31068, France, 33 5 61 29 48 51, r.ologeanu-taddei@tbs-education.fr %K eHealth %K mHealth %K usability %K management %K survey %K trust %K guidelines %K evaluation %D 2020 %7 18.11.2020 %9 Viewpoint %J JMIR Med Inform %G English %X This viewpoint argues that the clinical effects of mobile health (mHealth) interventions depends on the acceptance and adoption of these interventions and their mediators, such as usability of the mHealth software, software performance and features, training and motivation of patients and health care professionals to participate in the experience, or characteristics of the intervention (eg, personalized feedback). %M 33206060 %R 10.2196/21874 %U http://medinform.jmir.org/2020/11/e21874/ %U https://doi.org/10.2196/21874 %U http://www.ncbi.nlm.nih.gov/pubmed/33206060 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 10 %P e20265 %T Clinical Decision Support May Link Multiple Domains to Improve Patient Care: Viewpoint %A Kao,David %A Larson,Cynthia %A Fletcher,Dana %A Stegner,Kris %+ Department of Cardiology, University of Colorado School of Medicine, 12700 East 19th Avenue, Aurora, CO, 80045, United States, 1 720 848 5300, david.kao@cuanschutz.edu %K clinical decision support %K population medicine %K evidence-based medicine %K precision medicine %K care management %K electronic health records %D 2020 %7 16.10.2020 %9 Viewpoint %J JMIR Med Inform %G English %X Integrating clinical decision support (CDS) across the continuum of population-, encounter-, and precision-level care domains may improve hospital and clinic workflow efficiency. Due to the diversity and volume of electronic health record data, complexity of medical and operational knowledge, and specifics of target user workflows, the development and implementation of comprehensive CDS is challenging. Additionally, many providers have an incomplete understanding of the full capabilities of current CDS to potentially improve the quality and efficiency of care delivery. These varied requirements necessitate a multidisciplinary team approach to CDS development for successful integration. Here, we present a practical overview of current and evolving applications of CDS approaches in a large academic setting and discuss the successes and challenges. We demonstrate that implementing CDS tools in the context of linked population-, encounter-, and precision-level care provides an opportunity to integrate complex algorithms at each level into a unified mechanism to improve patient management. %M 33064106 %R 10.2196/20265 %U https://medinform.jmir.org/2020/10/e20265 %U https://doi.org/10.2196/20265 %U http://www.ncbi.nlm.nih.gov/pubmed/33064106 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 4 %P e17429 %T The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records %A Held,Philip %A Boley,Randy A %A Faig,Walter G %A O'Toole,John A %A Desai,Imran %A Zalta,Alyson K %A Khan,Jawad %A Sims,Shannon %A Brennan,Michael B %A Van Horn,Rebecca %A Glover,Angela C %A Hota,Bala N %A Patty,Brian D %A Rab,S Shafiq %A Pollack,Mark H %A Karnik,Niranjan S %+ Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, 325 S Paulina St, Suite 200, Chicago, IL, 60612, United States, 1 312 942 1423, philip_held@rush.edu %K electronic health record %K data collection %K veterans %D 2020 %7 6.4.2020 %9 Viewpoint %J JMIR Form Res %G English %X Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility. %M 32250276 %R 10.2196/17429 %U https://formative.jmir.org/2020/4/e17429 %U https://doi.org/10.2196/17429 %U http://www.ncbi.nlm.nih.gov/pubmed/32250276 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 5 %N 4 %P e50 %T Examining Tensions That Affect the Evaluation of Technology in Health Care: Considerations for System Decision Makers From the Perspective of Industry and Evaluators %A Desveaux,Laura %A Shaw,James %A Wallace,Ross %A Bhattacharyya,Onil %A Bhatia,R Sacha %A Jamieson,Trevor %+ Institute for Health Systems Solutions and Virtual Care, Women's College Hospital, 76 Grenville St, Toronto, ON, M5S 1B2, Canada, 1 416 323 6400 ext 8356, laura.desveaux@wchospital.ca %K technology %K evaluation %K policy %K healthcare %D 2017 %7 08.12.2017 %9 Viewpoint %J JMIR Med Inform %G English %X Virtual technologies have the potential to mitigate a range of challenges for health care systems. Despite the widespread use of mobile devices in everyday life, they currently have a limited role in health service delivery and clinical care. Efforts to integrate the fast-paced consumer technology market with health care delivery exposes tensions among patients, providers, vendors, evaluators, and system decision makers. This paper explores the key tensions between the high bar for evidence prior to market approval that guides health care regulatory decisions and the “fail fast” reality of the technology industry. We examine three core tensions: balancing user needs versus system needs, rigor versus responsiveness, and the role of pre- versus postmarket evidence generation. We use these to elaborate on the structure and appropriateness of evaluation mechanisms for virtual care solutions. Virtual technologies provide a foundation for personalized, patient-centered medicine on the user side, coupled with a broader understanding of impact on the system side. However, mechanisms for stakeholder discussion are needed to clarify the nature of the health technology marketplace and the drivers of evaluation priorities. %M 29222075 %R 10.2196/medinform.8207 %U http://medinform.jmir.org/2017/4/e50/ %U https://doi.org/10.2196/medinform.8207 %U http://www.ncbi.nlm.nih.gov/pubmed/29222075 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 5 %N 3 %P e28 %T Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation %A Yen,Po-Yin %A McAlearney,Ann Scheck %A Sieck,Cynthia J %A Hefner,Jennifer L %A Huerta,Timothy R %+ Washington University in St Louis, Institute for Informatics, 4444 Forest Park Ave, Suite 6318, St Louis, MO,, United States, 1 314 273 2213, yenp@wustl.edu %K health information technology %K adaptation %K adoption %K acceptance %D 2017 %7 07.09.2017 %9 Viewpoint %J JMIR Med Inform %G English %X In past years, policies and regulations required hospitals to implement advanced capabilities of certified electronic health records (EHRs) in order to receive financial incentives. This has led to accelerated implementation of health information technologies (HIT) in health care settings. However, measures commonly used to evaluate the success of HIT implementation, such as HIT adoption, technology acceptance, and clinical quality, fail to account for complex sociotechnical variability across contexts and the different trajectories within organizations because of different implementation plans and timelines. We propose a new focus, HIT adaptation, to illuminate factors that facilitate or hinder the connection between use of the EHR and improved quality of care as well as to explore the trajectory of changes in the HIT implementation journey as it is impacted by frequent system upgrades and optimizations. Future research should develop instruments to evaluate the progress of HIT adaptation in both its longitudinal design and its focus on adaptation progress rather than on one cross-sectional outcome, allowing for more generalizability and knowledge transfer. %M 28882812 %R 10.2196/medinform.7476 %U http://medinform.jmir.org/2017/3/e28/ %U https://doi.org/10.2196/medinform.7476 %U http://www.ncbi.nlm.nih.gov/pubmed/28882812 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 5 %N 3 %P e24 %T Clinical Note Creation, Binning, and Artificial Intelligence %A Deliberato,Rodrigo Octávio %A Celi,Leo Anthony %A Stone,David J %+ Harvard – MIT, Division of Health Science and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, MIT E25-505, Cambridge, MA, MA 02139, United States, 1 617 253 7937, lceli@mit.edu %K electronic health records %K artificial Intelligence %K clinical informatics %D 2017 %7 03.08.2017 %9 Viewpoint %J JMIR Med Inform %G English %X The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans. %M 28778845 %R 10.2196/medinform.7627 %U http://medinform.jmir.org/2017/3/e24/ %U https://doi.org/10.2196/medinform.7627 %U http://www.ncbi.nlm.nih.gov/pubmed/28778845 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 4 %N 2 %P e22 %T Data Safe Havens and Trust: Toward a Common Understanding of Trusted Research Platforms for Governing Secure and Ethical Health Research %A Lea,Nathan Christopher %A Nicholls,Jacqueline %A Dobbs,Christine %A Sethi,Nayha %A Cunningham,James %A Ainsworth,John %A Heaven,Martin %A Peacock,Trevor %A Peacock,Anthony %A Jones,Kerina %A Laurie,Graeme %A Kalra,Dipak %+ Institute of Health Informatics, University College London, The Farr Institute of Health Informatics Research, 222 Euston Road, London, NW1 2DA, United Kingdom, 44 20 3549 5293, n.lea@ucl.ac.uk %K trusted research platforms %K data safe havens %K trusted researchers %K legislative and regulatory compliance %K public engagement %K public involvement %K clinical research support %K health record linkage supported research %K genomics research support %D 2016 %7 21.06.2016 %9 Viewpoint %J JMIR Med Inform %G English %X In parallel with the advances in big data-driven clinical research, the data safe haven concept has evolved over the last decade. It has led to the development of a framework to support the secure handling of health care information used for clinical research that balances compliance with legal and regulatory controls and ethical requirements while engaging with the public as a partner in its governance. We describe the evolution of 4 separately developed clinical research platforms into services throughout the United Kingdom-wide Farr Institute and their common deployment features in practice. The Farr Institute is a case study from which we propose a common definition of data safe havens as trusted platforms for clinical academic research. We use this common definition to discuss the challenges and dilemmas faced by the clinical academic research community, to help promote a consistent understanding of them and how they might best be handled in practice. We conclude by questioning whether the common definition represents a safe and trustworthy model for conducting clinical research that can stand the test of time and ongoing technical advances while paying heed to evolving public and professional concerns. %M 27329087 %R 10.2196/medinform.5571 %U http://medinform.jmir.org/2016/2/e22/ %U https://doi.org/10.2196/medinform.5571 %U http://www.ncbi.nlm.nih.gov/pubmed/27329087 %0 Journal Article %@ 1929-073X %I JMIR Publications Inc. %V 5 %N 1 %P e5 %T An eHealth Platform to Manage Chronic Disease in Primary Care: An Innovative Approach %A Talboom-Kamp,Esther PWA %A Verdijk,Noortje A %A Harmans,Lara M %A Numans,Mattijs E %A Chavannes,Niels H %+ Saltro Diagnostic Centre, Mississippidreef 83, Utrecht, 3565 CE, Netherlands, 31 302361170, e.talboom@saltro.nl %K eHealth %K self-management %K anticoagulation clinic %K chronic obstructive pulmonary disease %K venous thromboembolism %K integrated disease management %K chronically ill %K telemonitoring %K primary care %D 2016 %7 09.02.2016 %9 Viewpoint %J Interact J Med Res %G English %X The number of individuals with chronic illness and multimorbidity is growing due to the rapid ageing of the population and the greater longevity of individuals. This causes an increasing workload in care, which results in a growing need for structural changes of the health care system. In recent years this led to a strong focus on promoting “self-management” in chronically ill patients. Research showed that patients who understand more about their disease, health, and lifestyle have better experiences and health outcomes, and often use less health care resources; the effect is even more when these patients are empowered to and responsible for managing their health and disease. In addition to the skills of patients, health care professionals need to shift to a role of teacher, partner, and professional supervisor of their patients. One way of supervising patients is by the use of electronic health (eHealth), which helps patients manage and control their disease. The application of eHealth solutions can provide chronically ill patients high-quality care, to the satisfaction of both patients and health care professionals, alongside a reduction in health care consumption and costs. %M 26860333 %R 10.2196/ijmr.4217 %U http://www.i-jmr.org/2016/1/e5/ %U https://doi.org/10.2196/ijmr.4217 %U http://www.ncbi.nlm.nih.gov/pubmed/26860333 %0 Journal Article %@ 2291-9694 %I Gunther Eysenbach %V 3 %N 4 %P e34 %T Disrupting Electronic Health Records Systems: The Next Generation %A Celi,Leo Anthony %A Marshall,Jeffrey David %A Lai,Yuan %A Stone,David J %+ Beth Israel Deaconess Medical Center, Division of Pulmonary, Critical Care, and Sleep Medicine, 330 Brookline Avenue, Boston, MA, 02215, United States, 1 617 667 5864, lceli@bidmc.harvard.edu %K clinical decision making %K clinical decision support %K electronic health records %K electronic notes %D 2015 %7 23.10.2015 %9 Viewpoint %J JMIR Med Inform %G English %X The health care system suffers from both inefficient and ineffective use of data. Data are suboptimally displayed to users, undernetworked, underutilized, and wasted. Errors, inefficiencies, and increased costs occur on the basis of unavailable data in a system that does not coordinate the exchange of information, or adequately support its use. Clinicians’ schedules are stretched to the limit and yet the system in which they work exerts little effort to streamline and support carefully engineered care processes. Information for decision-making is difficult to access in the context of hurried real-time workflows. This paper explores and addresses these issues to formulate an improved design for clinical workflow, information exchange, and decision making based on the use of electronic health records. %M 26500106 %R 10.2196/medinform.4192 %U http://medinform.jmir.org/2015/4/e34/ %U https://doi.org/10.2196/medinform.4192 %U http://www.ncbi.nlm.nih.gov/pubmed/26500106 %0 Journal Article %@ 2291-9694 %I Gunther Eysenbach %V 3 %N 3 %P e29 %T Optimizing Patient Preparation and Surgical Experience Using eHealth Technology %A Waller,Amy %A Forshaw,Kristy %A Carey,Mariko %A Robinson,Sancha %A Kerridge,Ross %A Proietto,Anthony %A Sanson-Fisher,Rob %+ University of Newcastle & Hunter Medical Research Institute, Health Behaviour Research Group, W4 HMRI Building, University of Newcastle, Callaghan, 2305, Australia, 61 2 4042 0708, amy.waller@newcastle.edu.au %K eHealth %K perioperative %K postoperative %K preoperative %K surgery %D 2015 %7 01.09.2015 %9 Viewpoint %J JMIR Med Inform %G English %X With population growth and aging, it is expected that the demand for surgical services will increase. However, increased complexity of procedures, time pressures on staff, and the demand for a patient-centered approach continue to challenge a system characterized by finite health care resources. Suboptimal care is reported in each phase of surgical care, from the time of consent to discharge and long-term follow-up. Novel strategies are thus needed to address these challenges to produce effective and sustainable improvements in surgical care across the care pathway. The eHealth programs represent a potential strategy for improving the quality of care delivered across various phases of care, thereby improving patient outcomes. This discussion paper describes (1) the key functions of eHealth programs including information gathering, transfer, and exchange; (2) examples of eHealth programs in overcoming challenges to optimal surgical care across the care pathway; and (3) the potential challenges and future directions for implementing eHealth programs in this setting. The eHealth programs are a promising alternative for collecting patient-reported outcome data, providing access to credible health information and strategies to enable patients to take an active role in their own health care, and promote efficient communication between patients and health care providers. However, additional rigorous intervention studies examining the needs of potential role of eHealth programs in augmenting patients’ preparation and recovery from surgery, and subsequent impact on patient outcomes and processes of care are needed to advance the field. Furthermore, evidence for the benefits of eHealth programs in supporting carers and strategies to maximize engagement from end users are needed. %M 26330206 %R 10.2196/medinform.4286 %U http://medinform.jmir.org/2015/3/e29/ %U https://doi.org/10.2196/medinform.4286 %U http://www.ncbi.nlm.nih.gov/pubmed/26330206 %0 Journal Article %@ 2291-9694 %I Gunther Eysenbach %V 2 %N 1 %P e4 %T Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records %A Harvey,Harlan %A Krishnaraj,Arun %A Alkasab,Tarik K %+ Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, FND 216, Boston, MA, 02116, United States, 1 617 724 4255, hbharvey@partners.org %K medical informatics %K medical records systems %K computerized %K health information management %D 2014 %7 11.03.2014 %9 Viewpoint %J JMIR Med Inform %G English %X As electronic medical records (EMRs) grow in size and complexity, there is increasing need for automated EMR tools that highlight the medical record items most germane to a practitioner’s task-specific needs. The development of such tools would be aided by gold standards of information relevance for a series of different clinical scenarios. We have previously proposed a process in which exemplar medical record data are extracted from actual patients’ EMRs, anonymized, and presented to clinical experts, who then score each medical record item for its relevance to a specific clinical scenario. In this paper, we present how that body of expert relevancy data can be used to create a test framework to validate new EMR search strategies. %M 25601018 %R 10.2196/medinform.3205 %U http://medinform.jmir.org/2014/1/e4/ %U https://doi.org/10.2196/medinform.3205 %U http://www.ncbi.nlm.nih.gov/pubmed/25601018