JMIR Human Factors最新文献

{"title":"An Informatics-Based, Payer-Led, Low-Intensity Multichannel Educational Campaign Designed to Decrease Postdischarge Utilization for Medicare Advantage Members: Retrospective Evaluation.","authors":"Danica Fernandes, Elise Kokonas, Jai Bansal, Ken Hayashima, Brian Hurley, Annabel Ryu, Snehal Mhatre, Mohammed Ghori, Kelly Jean Craig, Amanda L Zaleski, Lily Vogel, Alena Baquet-Simpson, Daniel Reif","doi":"10.2196/63841","DOIUrl":"https://doi.org/10.2196/63841","url":null,"abstract":"<p><strong>Background: </strong>Readmission avoidance initiatives have been a priority for the Centers for Medicare & Medicaid Services for over a decade; however, interventions are often high-intensity, costly, and resource-intensive, and therefore, rarely scalable or sustainable. Large national payers are in a unique position to leverage data to identify members in real-time who are at high risk of readmission to prioritize the scaled delivery of tailored behavior change techniques to provide an educational intervention to modify health behaviors.</p><p><strong>Objective: </strong>This study aims to examine the impact of an informatics-driven, multichannel educational messaging campaign implemented to decrease 30- and 90-day acute inpatient readmissions and emergency department (ED) visits among Medicare Advantage members of a large national payer.</p><p><strong>Methods: </strong>A quality improvement initiative was designed and implemented to provide an evidence-based outreach campaign using human-centered design and behavior change principles to deliver multiple intervention functions, including timely, contextual, and relevant delivery of education, enablement, and persuasion, to reinforce health-promoting behaviors related to planned or unplanned inpatient admissions. Outcomes, including 30- and 90-day acute inpatient readmissions and ED visits, were retrospectively evaluated from Medicare Advantage members enrolled in a large national health plan residing across the United States between May 2020 and July 2022. Leveraging utilization management data, rules-based logic identified members (N=368,393) with a planned acute inpatient procedure (ie, preadmission) or discharged from an acute hospital stay (ie, postdischarge) within 15 days. Members were sequentially assigned to a standard (N=141,223) or an enhanced (N=227,470) messaging group, whereby the standard group received usual outreach and the enhanced group received an educational intervention via a messaging campaign deployed through multiple low-intensity communication channels (eg, text message, email, direct mail) in addition to standard outreach.</p><p><strong>Results: </strong>Members who received enhanced outreach had fewer relative 30-day acute inpatient readmissions (-4.1%, 95% CI -5.5% to -2.7%; P<.001) and ED visits (-3.4%, 95% CI -5.0% to -1.7%; P<.001) compared with members receiving standard outreach. Similarly, these findings persisted for relative 90-day outcomes such that members receiving enhanced outreach experienced fewer acute inpatient readmissions (-5.4%, 95% CI -6.5% to -4.3%; P<.001) and ED visits (-3.8%, 95% CI -5.0% to -2.5%; P<.001) compared with members receiving standard outreach messaging.</p><p><strong>Conclusions: </strong>Behavior change techniques deployed via educational interventions as low-intensity multi-channel outreach is an effective strategy to reduce avoidable 30- and 90-day inpatient readmissions and ED visits in recently discharged Medicare A","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e63841"},"PeriodicalIF":2.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144162778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the usability of an HIV-prevention artificial intelligence chatbot in Malaysia: national observational study.","authors":"Zhao Ni, Sunyoung Oh, Rumana Saifi, Frederick Altice, Iskandar Azwa","doi":"10.2196/70034","DOIUrl":"https://doi.org/10.2196/70034","url":null,"abstract":"<p><strong>Background: </strong>Malaysia, an upper middle-income country in the Asia-Pacific region, has an HIV epidemic that has transitioned from needle sharing to sexual transmission, mainly in MSM. Men who have sex with men (MSM) are the most vulnerable population for HIV in Malaysia. In 2022, our team developed a web-based artificial intelligence (AI) chatbot and tested its feasibility and acceptability among MSM in Malaysia to promote HIV testing. To enhance the usability of the AI chatbot, we made it accessible to the public through the website called MYHIV365 and tested it in an observational study.</p><p><strong>Objective: </strong>This study aimed to test the usability of an AI chatbot in promoting HIV testing among MSM living in Malaysia.</p><p><strong>Methods: </strong>An observational study was conducted from August 2023 to March 2024 among 334 MSM. Participants were recruited through community outreach and social-networking apps using flyers. The interactions between participants and the AI chatbot were documented and retrieved from the chatbot developer's platform. Data were analyzed following a predefined metrics using R software (Posit Software, PBC, Boston, USA).</p><p><strong>Results: </strong>The AI chatbot interacted with 334 participants, assisting them in receiving free HIV self-testing kits, offering information on HIV, PrEP, and mental health, and providing details of 220 MSM-friendly clinics, including their addresses, phone numbers, and operating hours. After the study, 393 human-chatbot interactions were documented on the chatbot developer's platform. Most participants (304/334, 91%) interacted with the AI chatbot once, 30 (9%) engaged 2 or more times at different intervals. Participants' interaction time with the chatbot varied, ranging from 1 to 31 minutes. The AI chatbot properly addressed most participants' questions (362/393, 92.1%) about HIV and PrEP. However, in 31 interactions, participants posed additional questions to the chatbot that were not programmed into the chatbot algorithm, resulting in unanswered interactions.</p><p><strong>Conclusions: </strong>The web-based AI chatbot demonstrated high usability in delivering HIV self-testing kits and providing clinical information on HIV testing, PrEP, and mental health services. To enhance its usability in community and clinical settings, the chatbot must offer personalized health information and precise interaction, powered by a sophisticated machine learning algorithm. Additionally, establishing an effective connection between the AI chatbot and healthcare systems to eliminate stigma and discrimination towards MSM is crucial for the future implementation of AI chatbots.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Web-Based Lifestyle-Related Course for People Living With Multiple Sclerosis: Quantitative Evaluation of Course Completion, Satisfaction, and Lifestyle Changes Among Participants Enrolled in a Randomized Controlled Trial.","authors":"Maggie Yu, Sandra Neate, Steve Simpson-Yap, Rebekah Davenport, William Bevens, George Jelinek, Jeanette Reece","doi":"10.2196/59363","DOIUrl":"https://doi.org/10.2196/59363","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Web-based health courses providing lifestyle-related information can potentially increase knowledge, facilitate behavior change, and improve health outcomes for people living with multiple sclerosis (MS). Despite the low engagement with web-based programs by this population, few studies have evaluated factors influencing engagement. This study evaluated engagement with our 6-week lifestyle-related course (Multiple Sclerosis Online Course; MSOC) by participants enrolled in a large, international randomized controlled trial, as well as preliminary outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to quantitatively assess engagement with the MSOC (the intervention course [IC] and standard-care course [SCC]), motivators of and barriers to participants' course completion, course satisfaction, engagement with the community forum, and intentions to implement lifestyle changes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We collected data via a baseline survey before course commencement and an evaluation survey 1 month after the 6-week course. Course completers were queried on motivators of completion, course satisfaction, previous knowledge, forum participation, and intentions to adopt lifestyle changes. Noncompleters were queried on barriers to course completion. Differences between the 2 study arms were examined using chi-square and 2-tailed t tests. Multivariable linear regression models assessed factors (sociodemographic and course and health related) associated with participants' intentions to adopt lifestyle changes adjusting for baseline lifestyle factors. Moderation analyses were conducted to test group differences.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 857 participants, 442 (51.6%) completed the MSOC (IC: n=218, 49.3%; SCC: n=224, 50.7%), and 291 (34%) completed the evaluation survey (n=254, 87.3% course completers; n=37, 12.7% noncompleters). Key motivators of course completion included an interest in participating in MS research, optimizing health, course flexibility, and relevant and useful course content. Barriers to course completion included time constraints and technical issues. Most course completers rated the MSOC as \"excellent/very good\" (IC: 92/126, 73%; SCC: 78/128, 60.9%; P=.17). Engagement with the facilitator-led community forum was higher in the IC than in the SCC (56/126, 44.4% vs 32/128, 25%; P=.003). More IC completers versus SCC completers expressed their intention to adopt dietary changes (89/125, 71.2% vs 74/127, 58.3%; P=.04), increase their sun exposure (82/124, 66.1% vs 62/124, 50%; P=.01), supplement with omega-3 (84/125, 67.2% vs 60/126, 47.6%; P=.004), and practice meditation (85/124, 68.5% vs 66/126, 52.4%; P=.009). Forum engagement, course satisfaction, new course content, and an interest in receiving additional course content were associated with intentions to adopt lifestyle changes across both study arms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The web-based lifestyle IC provided new ","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e59363"},"PeriodicalIF":2.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study.","authors":"Mai Ikegaya, Jerome Clifford Foo, Taiga Murata, Kenta Oshima, Jinhyuk Kim","doi":"10.2196/66750","DOIUrl":"https://doi.org/10.2196/66750","url":null,"abstract":"<p><strong>Background: </strong>While the health benefits of physical activity are well-known, adherence to regular physical activity remains a major challenge. Just-in-time adaptive intervention (JITAI) has been proposed as one method to increase physical activity by delivering an intervention at a time when individuals are more likely to make behavioral changes. However, most studies that have implemented JITAI have used uniform intervention criteria (UIC) across participants rather than personalized intervention criteria (PIC) for the individual.</p><p><strong>Objective: </strong>The objective of this paper was to examine the effectiveness of using JITAI implemented with PIC to increase physical activity.</p><p><strong>Methods: </strong>Healthy university students wore a wrist activity monitor for 2 weeks. Participants were divided into 2 groups, which received JITAI to promote physical activity according to either PIC or UIC. In the first week, the mean distance moved and sedentary time per hour for each participant were calculated to derive PIC. UIC was obtained from a 2-week study with a different sample (n=47) conducted under the same conditions. In the second week, JITAI prompts were sent every hour if both of the following criteria were met: the distance moved was shorter, and sedentary time was longer than PIC or UIC. Differences in changes in physical activity as a result of implementing interventions according to PIC and UIC were analyzed using multilevel models.</p><p><strong>Results: </strong>We analyzed data from 28 healthy university students (18-23 y old, female n=12). Both PIC (P<.001) and UIC (P<.001) significantly increased physical activity in the first hour after JITAI was received. In that first hour, PIC increased physical activity more than UIC; more calories were burned (P=.02), more steps were taken (P=.007), and distance moved was increased (P=.003). However, over the course of the week, the use of JITAI did not significantly increase physical activity levels.</p><p><strong>Conclusions: </strong>Our results appear to suggest that PIC-based JITAI is more effective than UIC-based JITAI, consistent with the idea of a need for precision health approaches. Further research is needed to develop effective long-term intervention designs with sustainable effects.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e66750"},"PeriodicalIF":2.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User Intent to Use DeepSeek for Health Care Purposes and Their Trust in the Large Language Model: Multinational Survey Study.","authors":"Avishek Choudhury, Yeganeh Shahsavar, Hamid Shamszare","doi":"10.2196/72867","DOIUrl":"https://doi.org/10.2196/72867","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative artificial intelligence (AI)-particularly large language models (LLMs)-has generated unprecedented interest in applications ranging from everyday questions and answers to health-related inquiries. However, little is known about how everyday users decide whether to trust and adopt these technologies in high-stakes contexts such as personal health.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;This study examines how ease of use, perceived usefulness, and risk perception interact to shape user trust in and intentions to adopt DeepSeek, an emerging LLM-based platform, for health care purposes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We adapted survey items from validated technology acceptance scales to assess user perception of DeepSeek. A 12-item Likert scale questionnaire was developed and pilot-tested (n=20). It was then distributed on the web to users in India, the United Kingdom, and the United States who had used DeepSeek within the past 2 weeks. Data analysis involved descriptive frequency assessments and Partial Least Squares Structural Equation Modeling. The model assessed direct and indirect effects, including potential quadratic relationships.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 556 complete responses were collected, with respondents almost evenly split across India (n=184), the United Kingdom (n=185), and the United States (n=187). Regarding AI in health care, when asked whether they were comfortable with their health care provider using AI tools, 59.3% (n=330) were fine with AI use provided their doctor verified its output, and 31.5% (n=175) were enthusiastic about its use without conditions. DeepSeek was used primarily for academic and educational purposes, 50.7% (n=282) used DeepSeek as a search engine, and 47.7% (n=265) used it for health-related queries. When asked about their intent to adopt DeepSeek over other LLMs such as ChatGPT, 52.1% (n=290) were likely to switch, and 28.9% (n=161) were very likely to do so. The study revealed that trust plays a pivotal mediating role; ease of use exerts a significant indirect impact on usage intentions through trust. At the same time, perceived usefulness contributes to trust development and direct adoption. By contrast, risk perception negatively affects usage intent, emphasizing the importance of robust data governance and transparency. Significant nonlinear paths were observed for ease of use and risk, indicating threshold or plateau effects.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Users are receptive to DeepSeek when it is easy to use, useful, and trustworthy. The model highlights trust as a mediator and shows nonlinear dynamics shaping AI-driven health care tool adoption. Expanding the model with mediators such as privacy and cultural differences could provide deeper insights. Longitudinal experimental designs could establish causality. Further investigation into threshold and plateau phenomena could refine our understanding of user perceptions as they become m","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e72867"},"PeriodicalIF":2.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Just-in-Time Adaptive Intervention (Shift) to Manage Problem Anger After Trauma: Co-Design and Development Study.","authors":"Olivia Metcalf, David Forbes, Lauren M Henry, Tianchen Qian, Tracey Varker, Melissa A Brotman, Sean Cowlishaw, Karen E Lamb, Meaghan L O'Donnell","doi":"10.2196/62960","DOIUrl":"https://doi.org/10.2196/62960","url":null,"abstract":"<p><strong>Background: </strong>Problem anger is common after experiencing trauma and is under-recognized relative to other posttraumatic mental health issues. Previous research has shown that digital mental health tools have significant potential to support individuals with problem anger after trauma.</p><p><strong>Objective: </strong>The objective of this study was to describe the co-design and development of a just-in-time adaptive intervention (JITAI) targeting problem anger in individuals who have experienced trauma.</p><p><strong>Methods: </strong>We used a participatory design process following the double-diamond framework. Phase 1 involved one-on-one qualitative interviews with trauma-exposed individuals with problem anger (n=10). Using an inductive approach (interpretative phenomenological analysis), we thematically coded interview data to create design principles for this population and generate potential content for the intervention. Phase 2 involved academic and clinical experts in trauma and experts in digital health reviewing the Phase 1 results and an evidence-based cognitive behavioral approach to treating anger. We then created intervention content and prototypes, which we then took to workshops with all participants for feedback, using group discussions and ratings of desirability and feasibility.</p><p><strong>Results: </strong>From Phase 1, core considerations for a JITAI included look and feel preferences, self-led and personalized support and content, and different support needed for each anger stage. A JITAI was developed with the following components: (1) personalized schedules and content onboarding; (2) psychoeducation about problem anger; (3) crisis support; (4) mood monitoring via anger check-ins; (5) self-led and personalized circuit breakers; (6) cognitive-behavioral based skills; (7) and a digital Coach embedded in the app. Some suggested features, such as social networking and sharing data with loved ones, were not pursued due to feasibility reasons relating to participant safety or technical costs.</p><p><strong>Conclusions: </strong>The resulting JITAI, termed \"Shift,\" is the first digital mental health tool designed with end users to manage anger after trauma.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e62960"},"PeriodicalIF":2.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Health Literacy in Adults With Low Reading and Writing Skills Living in Germany: Mixed Methods Study.","authors":"Saskia Muellmann, Rebekka Wiersing, Hajo Zeeb, Tilman Brand","doi":"10.2196/65345","DOIUrl":"10.2196/65345","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital health literacy is a key factor in enabling users to navigate in an increasingly digitalized health care system. Low levels of digital health literacy are associated with higher age, low education, and income, as well as low functional health literacy. Around 6.2 million adults living in Germany have low reading and writing skills. Due to their low literacy, this group is often underrepresented in research studies and therefore little is known about their digital health literacy and use of digital health tools.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objectives of this study were to assess digital health literacy in adults with low reading and writing skills and to explore which digital health tools they use in daily life.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;An interviewer-administered survey and focus groups were conducted with adult residents of Bremen, Germany, who were aged 18-64 years and had low reading and writing skills. In addition, a stakeholder workshop was held to derive recommendations on how digital health literacy could be improved. The survey questionnaire included 21 items addressing the use of digital health technologies and digital health literacy (eHealth Literacy Scale). Focus group participants completed several tasks on web-based health information and then discussed their experiences. Survey data were analyzed using descriptive statistics and linear regression. Qualitative content analysis was applied to analyze the focus group data and the written documentation of the stakeholder workshop.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Survey participants (n=96) were on average 43 (SD 10.7) years old, 72% (69/96) were female, and 92% (88/96) were not born in Germany. Participants reported mainly using information-related digital health technologies such as health apps (40/96, 42%), health websites (30/96, 31%), or activity trackers (27/96, 28%). The mean digital health literacy score was 22 (SD 8) points, with 35% (34/96) of participants classified as having a low digital health literacy (score between 8-19/40 points). Digital health technology use was associated with higher digital health literacy. For participants in the 5 focus groups (total n=39; mean age 43, SD 12.6 years; n=34, 87% female), limited technical skills and language problems were the most important challenges. Furthermore, focus group participants reported that they favor videos when searching for web-based health information and prefer to seek support from family members or local organizations for health issues. Stakeholders (n=15) recommended that health websites should be available in multiple languages, contain simple and easy-to-read language, and use images, symbols, and videos.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While adults with low reading and writing skills use digital health technologies, many find it challenging to search for health information on the internet due to lacking technical skills and language problems. To ensur","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e65345"},"PeriodicalIF":2.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Auxiliary Teaching and Student Evaluation Methods Based on Facial Expression Recognition in Medical Education.","authors":"Xueling Zhu, Roben A Juanatas","doi":"10.2196/72838","DOIUrl":"10.2196/72838","url":null,"abstract":"<p><strong>Unlabelled: </strong>Traditional medical education encounters several challenges. The introduction of advanced facial expression recognition technology offers a new approach to address these issues. The aim of the study is to propose a medical education-assisted teaching and student evaluation method based on facial expression recognition technology. This method consists of 4 key steps. In data collection, multiangle high-definition cameras record students' facial expressions to ensure data comprehensiveness and accuracy. Facial expression recognition uses computer vision and deep learning algorithms to identify students' emotional states. The result analysis stage organizes and statistically analyzes the recognized emotional data to provide teachers with students' learning status feedback. In the teaching feedback stage, teaching strategies are adjusted according to the analysis results. Although this method faces challenges such as technical accuracy, device dependency, and privacy protection, it has the potential to improve teaching effectiveness, optimize personalized learning, and promote teacher-student interaction. The application prospects of this method in medical education are broad, and it is expected to significantly enhance teaching quality and students' learning experience.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e72838"},"PeriodicalIF":2.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Dermatologists' Acceptance of Digital Health Interventions: Cross-Sectional Survey and Cluster Analysis.","authors":"Patrick Reinders, Matthias Augustin, Marina Otten","doi":"10.2196/59757","DOIUrl":"10.2196/59757","url":null,"abstract":"<p><strong>Background: </strong>Digital health interventions (DHIs) have the potential to enhance dermatological care by improving quality, patient empowerment, and efficiency. However, adoption remains limited, particularly in Germany.</p><p><strong>Objective: </strong>This study explores German dermatologists' attitudes toward DHIs, clustering them by acceptance levels and analyzing differences in sociodemographics and current and future DHI use.</p><p><strong>Methods: </strong>We conducted a cross-sectional survey, randomly inviting 1000 dermatologists in Germany to participate. The questionnaire consisted of Likert scale items rating the acceptability of DHIs from 1 to 5. Items on the current and future use of DHIs were also included. Exploratory factor analysis was used to identify factors and reduce data as input for a 2-step clustering algorithm.</p><p><strong>Results: </strong>The survey with 170 dermatologists (mean age 50.8, SD 10.3 y; 74/167, 55.7% female) identified four factors through the exploratory factor analysis: (1) \"Positive Expectancies and Acceptability of DHIs,\" (2) \"Dermatologists' Digital Competencies,\" (3) \"Negative Expectancies and Barriers,\" and (4) \"Dermatologists' Perspectives on Patients' Acceptability and Competencies.\" The analysis identified three distinct clusters: (1) Indecisives (n=69)-moderate intentions to use DHIs and moderate negative expectations toward them; (2) Adopters (n=60)-high intentions to use DHIs and high digital competencies; and (3) Rejectors (n=26)-low intentions to use DHIs and low digital competencies. Adopters were significantly younger, more often based in urban centers, and exhibited the highest adoption rates of DHIs compared to the other clusters. Across all clusters, inadequate reimbursement and perceived structural barriers were cited as significant challenges to DHI adoption. Still, only one-third of the Adopters used DHIs including teledermatology or artificial intelligence.</p><p><strong>Conclusions: </strong>Dermatologists in Germany exhibited varied levels of acceptance and readiness for DHIs, with demographic and structural factors influencing adoption. Addressing barriers such as reimbursement and investing in digital literacy could promote wider use, potentially reducing health inequalities by improving access to digital health care.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e59757"},"PeriodicalIF":2.6,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12118942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized Digital Care Pathways Enable Enhanced Patient Management as Perceived by Health Care Professionals: Mixed-Methods Study.","authors":"David Rodrigues, Clara Jasmins, Ricardo Ladeiras-Lopes, Luis Patrao, Eduardo Freire Rodrigues","doi":"10.2196/68581","DOIUrl":"10.2196/68581","url":null,"abstract":"<p><strong>Background: </strong>Clinical decision support systems are known to improve adherence to clinical practice guidelines and patient outcomes by providing clinicians with timely, accurate, and appropriate knowledge.</p><p><strong>Objective: </strong>This study investigates the perceived usefulness and practical implementation of UpHill Route v3, a personalized digital care pathway (PDCP) system, in enhancing clinical decision-making and patient management across various clinical settings.</p><p><strong>Methods: </strong>A mixed-methods retrospective study was conducted among medical doctors and nurses from four National Health System-Local Health Units in Portugal. Data were collected from May 2023 to April 2024. The primary data source was an anonymous questionnaire assessing health care professionals' perceptions of UpHill Route v3's usefulness using the Likert scale ranging from 0 (do not agree) to 10 (totally agree). Secondary analysis involved quantifying decisions across heart failure, multimorbidity, diabetes, and colorectal and breast cancer clinical pathways. These data were collected from user interactions with UpHill Route v3 as well as from its internal database. Descriptive and bivariate statistics were used to analyze the data.</p><p><strong>Results: </strong>A total of 22 health care professionals with mean age 44.7 (SD 10.6) years, including 15 (68%) female participants and 9 (41%) physicians were included in the study. High ratings for adherence to clinical protocols, mean score 8.06 (SD 1.73); clinical decision support, mean score 8.05 (SD 1.73); patient care improvement, mean score 7.63 (SD 2.22); and confidence in patient management, mean score 8.26 (SD 1.56) were reported. Secondary analysis showed that across 3574 patients, 25,741 clinical decisions were informed, and 9254 actions were performed with the assistance of the PDCP tool.</p><p><strong>Conclusions: </strong>The UpHill Route v3 PDCP tool is highly valued by health care professionals for its ability to support clinical decision-making and improve operational efficiency across various clinical settings. Our findings suggest that this tool can effectively bridge the gap between clinical guidelines and real-world practice.</p>","PeriodicalId":36351,"journal":{"name":"JMIR Human Factors","volume":"12 ","pages":"e68581"},"PeriodicalIF":2.6,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12097650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}