Journal of Participatory Medicine最新文献

{"title":"Subjective Performance Expectations From and Demographic and Categorical Differences in the Acceptance of Virtual Reality or AI Technologies in Rehabilitation Programs: Cross-Sectional Questionnaire Survey With Rehabilitation Patients.","authors":"Guido Waldmann, Dominik Raab","doi":"10.2196/69350","DOIUrl":"10.2196/69350","url":null,"abstract":"<p><strong>Background: </strong>More than a few concepts have been presented in rehabilitation clinics that implement aspects of modern IT in the arrangement of augmented reality or virtual rehabilitation aiming to enhance cognitive or motor learning and rehabilitation motivation. Despite their scientific success, it is currently unknown whether rehabilitants will accept rehabilitation concepts that integrate modern ITs.</p><p><strong>Objective: </strong>This study aims to investigate the subjective performance expectations of rehabilitation patients regarding the application of virtual reality (VR) or artificial intelligence technologies across various therapeutic fields, and to identify demographic and categorical differences in acceptance to inform the development and implementation of VR-based rehabilitation programs.</p><p><strong>Methods: </strong>In total, 111 rehabilitation patients were surveyed about their subjective performance expectations of VR in 15 therapeutic fields with a questionnaire. The distribution of the responses was evaluated using box plots. The relationship between the subjective performance expectations for the 15 therapeutic fields was analyzed using the Spearman ρ coefficient, while the Mann-Whitney U test was used to compare subjective performance expectations between age groups and between genders.</p><p><strong>Results: </strong>For all 15 therapeutic fields, the median of the subjective performance expectations was between 2 and 3, while therapeutic fields in the categories \"activity/movement,\" \"competence in daily life/communication,\" and \"education\" tended to be rated higher than therapeutic fields in the categories \"relaxation/passive measures\" and \"advisory/conversation.\" A significant rank correlation was observed for 103 out of 105 pairwise comparisons of the therapeutic fields, with distinct patterns of effects sizes within the chosen categories. There was no significant difference in the evaluation between rehabilitants of employable age and those aged 68 years or older. Male rehabilitation patients reported greater subjective expectations for virtual rehabilitation than female patients, but there was only a significant difference with small effect sizes for 3 of the 15 therapeutic fields.</p><p><strong>Conclusions: </strong>The general trend is that patients can imagine taking part in VR in rehabilitation activities involving active movement (physiotherapy, sports and exercise therapy, and occupational therapy) and health education. The results of the survey show that there is also a high level of support for the therapeutic field advisory/conversation. Current circumstances have led to substantial use of virtual offerings in practice. The limited data available may have encouraged the professional development of VR systems and their widespread use in medical rehabilitation follow-up in the home setting.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e69350"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12333460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800503","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":"Consumer Data is Key to Artificial Intelligence Value: Welcome to the Health Care Future.","authors":"James Cummings","doi":"10.2196/68261","DOIUrl":"10.2196/68261","url":null,"abstract":"<p><strong>Unlabelled: </strong>Humanity stands at the threshold of a new era in biological understanding, disease treatment, and overall wellness. The convergence of evolving patient and caregiver (consumer) behaviors, increased data collection, advancements in health technology and standards, federal policies, and the rise of artificial intelligence (AI) is driving one of the most significant transformations in human history. To achieve transformative health care insights, AI must have access to comprehensive longitudinal health records (LHRs) that span clinical, genomic, nonclinical, wearable, and patient-generated data. Despite the extensive use of electronic medical records and widespread interoperability efforts, current health care organizations, electronic medical record vendors, and public agencies are not incentivized to develop and maintain complete LHRs. This paper explores the new paradigm of consumers as the common provenance and singular custodian of LHRs. With fully aligned intentions and ample time to dedicate to optimizing their health outcomes, patients and caregivers must assume the sole responsibility to manage or delegate aggregation of complete, accurate, and real-time LHRs. Significant gaps persist in empowering consumers to act as primary custodians of their health data and to aggregate their complete LHRs, a foundational requirement for the effective application of AI. Rare disease communities, leaders in participatory care, offer a compelling model for demonstrating how consumer-driven data aggregation can be achieved and underscore the need for improved policy frameworks and technological tools. The convergence of AI and LHRs promises to transform medicine by enhancing clinical decision-making, accelerating accurate diagnoses, and dramatically advancing our ability to understand and treat disease at an unprecedented pace.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e68261"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12316320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144765604","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":"Integrating Patient Choice and Collaborative Care Managers to Implement eHealth Tools in Depression: Self-Report Pilot Study.","authors":"Jennifer Severe, Adrienne Lapidos, Danielle S Taubman, Amy Sochowicz, Sophia Wolk, Daniela Lopez, Abigail Biehl, Sagar V Parikh","doi":"10.2196/55349","DOIUrl":"10.2196/55349","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Improving mental health treatment within the collaborative care model (CoCM) may be achieved by using e-mental health (e-MH) tools and addressing the challenges to their integration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to understand how patients select, engage, and use three self-help e-MH tools for depression, and to explore satisfaction with e-MH tools, with a particular emphasis on care manager interactions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was a single-center, nonrandomized, preferred assignment study of two cognitive behavioral therapy-based tools (Moodkit and moodgym) and an educational website (the Depression Center Toolkit). The tools were recommended for use in 15-minute sessions 3 times a week, for 6 weeks, coupled with low-intensity care manager coaching. Utilization of e-MH was also captured during an additional 4 weeks without coaching. Self-report outcome measures were gathered at baseline, weekly for 6 weeks, at week 10, and through activities suggested by the tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The 32 participants enrolled were predominantly female (n=27, 84%), non-Hispanic Caucasian (n=29, 91%), with a mean age of 41.8 (SD 16.1; range 20 to 78) years. Most participants (n=26, 81%) presented with moderate to moderately severe depression (Patient Health Questionnaire-9=11-19) and a marked level of impairment in different areas of functioning. About 81% (n=26) of the participants initially selected a cognitive behavioral therapy-based tool, and 19% (n=6) selected the educational website. In total, 4 of 32 (12%) participants switched tools within the first week, 6 of 32 (22%) participants dropped out, and one was removed. The remaining 25 active individuals used tools on average 3.0 (SD 2.4) times per week, most time (67%), for 11 to 20 minutes or more at a time. Of the 19 participants reached and surveyed at week 6, 52% (16/31) remained actively engaged with their tools, including 2 users who had switched tools and 8 between 45 and 78 years old. At week 10, about 75% (12/16) of this subgroup were using their tools with no coaching; this represented 49% of the cohort. Satisfaction increased with progressive use of the tool. The care manager's low-intensity coaching lasted on average 7.9 (SD 3.9) minutes and promoted better understanding and greater use of the tools. Other facilitators to adherence consisted of organization, convenience, ease, accessibility, and privacy policies of the tools, while barriers included time constraints, depressive symptoms, and uncertainty about the efficacy of the tool.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Uptake of e-MH tools for depression is feasible and associated with significant user satisfaction in CoCM. Low-intensity care manager coaching is consistent with the CoCM and is associated with uptake and ongoing use of e-MH tools. To our knowledge, this is the first study to leverage the care manager's proactive outreach to and routine follow-ups wit","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e55349"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12313309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761572","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":"Patient and Practitioner Perspectives on the Definition and Measurement of Therapeutic Empathy: Qualitative Study.","authors":"Amber Bennett-Weston, Jeremy Howick","doi":"10.2196/71610","DOIUrl":"10.2196/71610","url":null,"abstract":"<p><strong>Background: </strong>Most definitions of therapeutic empathy are based on practitioners' perspectives, and few account for patients' views. Therefore, we do not understand what therapeutic empathy means to patients. Given that therapeutic empathy involves a relationship between patients and practitioners, the underrepresentation of the patient voice threatens to undermine the validity of therapeutic empathy definitions and subsequently, how the concept is measured, taught, and practiced.</p><p><strong>Objective: </strong>The aim of the study is to explore the perspectives of patients and practitioners on the definition of therapeutic empathy and how it should therefore be measured.</p><p><strong>Methods: </strong>A qualitative study, underpinned by a social constructivist stance, was conducted. Patients and practitioners were purposively sampled from a medical school and a school of health care to represent a diversity of lived experiences and health care professions. In-depth, semistructured interviews were undertaken, and the data were analyzed using reflexive thematic analysis. Data collection ceased upon reaching meaning saturation.</p><p><strong>Results: </strong>In total, 16 participants (8 patients and 8 practitioners) were interviewed in June and July 2024. Reflexive thematic analysis generated three overarching themes that synthesize the views of patients and practitioners on therapeutic empathy and how it should be measured: (1) therapeutic empathy involves the practitioner showing the patient (that they are interested in the patient as a person, that they are actively listening, that they understand, that they are emotionally engaged, and that they are responding to their needs), (2) context matters (eg, the clinical scenario, time, and the patient), and (3) short, simple scales are a pragmatic approach to measurement.</p><p><strong>Conclusions: </strong>Patients and practitioners have similar views about what empathy is and define therapeutic empathy as involving the practitioner demonstrating specific attitudes and behaviors to their patients. These attitudes and behaviors should be included in interventions to enhance therapeutic empathy and in measures of the concept. However, contextual factors may influence the expression of therapeutic empathy in practice. The findings highlight the need for, and can inform the development of, a short therapeutic empathy scale that allows the comparison of scores between patients, practitioners, students, and observers.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e71610"},"PeriodicalIF":0.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12313346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761573","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":"Patient Perspectives on Artificial Intelligence in Medical Imaging.","authors":"Jeffry Glenning, Lisa Gualtieri","doi":"10.2196/67816","DOIUrl":"10.2196/67816","url":null,"abstract":"<p><strong>Unlabelled: </strong>Artificial intelligence (AI) is reshaping medical imaging with the promise of improved diagnostic accuracy and efficiency. Yet, its ethical and effective adoption depends not only on technical excellence but also on aligning implementation with patient perspectives. This commentary synthesizes emerging research on how patients perceive AI in radiology, expressing cautious optimism, a desire for transparency, and a strong preference for human oversight. Patients consistently view AI as a supportive tool rather than a replacement for clinicians. We argue that centering patient voices is essential to sustaining trust, preserving the human connection in care, and ensuring that AI serves as a truly patient-centered innovation. The path forward requires participatory approaches, ethical safeguards, and transparent communication to ensure that AI enhances, rather than diminishes, the values patients hold most dear.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e67816"},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144733668","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":"Patient Perspectives on Artificial Intelligence in Health Care: Focus Group Study for Diagnostic Communication and Tool Implementation.","authors":"Garrett Foresman, Joshua Biro, Alberta Tran, Kate MacRae, Sadaf Kazi, Laura Schubel, Adam Visconti, William Gallagher, Kelly M Smith, Traber Giardina, Helen Haskell, Kristen Miller","doi":"10.2196/69564","DOIUrl":"10.2196/69564","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is rapidly transforming health care, offering potential benefits in diagnosis, treatment, and workflow efficiency. However, limited research explores patient perspectives on AI, especially in its role in diagnosis and communication. This study examines patient perceptions of various AI applications, focusing on the diagnostic process and communication.</p><p><strong>Objective: </strong>This study aimed to examine patient perspectives on AI use in health care, particularly in diagnostic processes and communication, identifying key concerns, expectations, and opportunities to guide the development and implementation of AI tools.</p><p><strong>Methods: </strong>This study used a qualitative focus group methodology with co-design principles to explore patient and family member perspectives on AI in clinical practice. A single 2-hour session was conducted with 17 adult participants. The session included interactive activities and breakout sessions focused on five specific AI scenarios relevant to diagnosis and communication: (1) portal messaging, (2) radiology review, (3) digital scribe, (4) virtual human, and (5) decision support. The session was audio-recorded and transcribed, with facilitator notes and demographic questionnaires collected. Data were analyzed using inductive thematic analysis by 2 independent researchers (GF and JB), with discrepancies resolved via consensus.</p><p><strong>Results: </strong>Participants reported varying comfort levels with AI applications contingent on the level of patient interaction, with digital scribe (average 4.24, range 2-5) and radiology review (average 4.00, range 2-5) being the highest, and virtual human (average 1.68, range 1-4) being the lowest. In total, five cross-cutting themes emerged: (1) validation (concerns about model reliability), (2) usability (impact on diagnostic processes), (3) transparency (expectations for disclosing AI usage), (4) opportunities (potential for AI to improve care), and (5) privacy (concerns about data security). Participants valued the co-design session and felt they had a significant say in the discussions.</p><p><strong>Conclusions: </strong>This study highlights the importance of incorporating patient perspectives in the design and implementation of AI tools in health care. Transparency, human oversight, clear communication, and data privacy are crucial for patient trust and acceptance of AI in diagnostic processes. These findings inform strategies for individual clinicians, health care organizations, and policy makers to ensure responsible and patient-centered AI deployment in health care.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e69564"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144699783","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":"Development of a Cocreated Decision Aid for Patients With Depression-Combining Data-Driven Prediction With Patients' and Clinicians' Needs and Perspectives: Mixed Methods Study.","authors":"Kaying Kan, Frederike Jörg, Klaas J Wardenaar, Frank J Blaauw, Maarten F Brilman, Ellen Visser, Dennis Raven, Dwayne Meijnckens, Erik Buskens, Danielle C Cath, Bennard Doornbos, Robert A Schoevers, Talitha L Feenstra","doi":"10.2196/67170","DOIUrl":"10.2196/67170","url":null,"abstract":"<p><strong>Background: </strong>Major depressive disorders significantly impact the lives of individuals, with varied treatment responses necessitating personalized approaches. Shared decision-making (SDM) enhances patient-centered care by involving patients in treatment choices. To date, instruments facilitating SDM in depression treatment are limited, particularly those that incorporate personalized information alongside general patient data and in cocreation with patients.</p><p><strong>Objective: </strong>This study outlines the development of an instrument designed to provide patients with depression and their clinicians with (1) systematic information in a digital report regarding symptoms, medical history, situational factors, and potentially successful treatment strategies and (2) objective treatment information to guide decision-making.</p><p><strong>Methods: </strong>The study was co-led by researchers and patient representatives, ensuring that all decisions regarding the development of the instrument were made collaboratively. Data collection, analyses, and tool development occurred between 2017 and 2021 using a mixed methods approach. Qualitative research provided insight into the needs and preferences of end users. A scoping review summarized the available literature on identified predictors of treatment response. K-means cluster analysis was applied to suggest potentially successful treatment options based on the outcomes of similar patients in the past. These data were integrated into a digital report. Patient advocacy groups developed treatment option grids to provide objective information on evidence-based treatment options.</p><p><strong>Results: </strong>The Instrument for shared decision-making in depression (I-SHARED) was developed, incorporating individual characteristics and preferences. Qualitative analysis and the scoping review identified 4 categories of predictors of treatment response. The cluster analysis revealed 5 distinct clusters based on symptoms, functioning, and age. The cocreated I-SHARED report combined all findings and was integrated into an existing electronic health record system, ready for piloting, along with the treatment option grids.</p><p><strong>Conclusions: </strong>The collaboratively developed I-SHARED tool, which facilitates informed and patient-centered treatment decisions, marks a significant advancement in personalized treatment and SDM for patients with major depressive disorders.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e67170"},"PeriodicalIF":0.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683326","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":"Proof of Concept for a Digital Framework to Support a Shared Agenda at Surgical Ward Rounds: Participatory Design Study.","authors":"Helle Poulsen, Jane Clemensen, Jette Ammentorp, Poul-Erik Kofoed, Maiken Wolderslund","doi":"10.2196/69679","DOIUrl":"10.2196/69679","url":null,"abstract":"<p><strong>Background: </strong>Surgical ward rounds (SWRs) are often unstructured and deprioritized compared to traditional surgical tasks, leading to limited interdisciplinary collaboration, unprepared patients, and low family attendance.</p><p><strong>Objective: </strong>This study aims to co-design and develop a digital framework to facilitate a shared agenda for SWRs, ensuring all core participants can attend and participate effectively.</p><p><strong>Methods: </strong>Participatory design (PD) methodologies were used, using user-engaging activities within an iterative process. A multidisciplinary team, including patients, relatives, health care providers, technology designers, and researchers, collaborated in workshops and testing to translate user needs into prototypes of technologies consisting of the digital framework.</p><p><strong>Results: </strong>A logistics system was developed for nurses to prebook the SWRs in designated time slots, enabling them to prepare relevant data and partake in the dialogue with patients. In addition, a mobile health (mHealth) app displayed the schedule for patients and relatives, helping them to participate and prepare questions in advance. Multiple iterations ensured that the digital framework met user needs and was feasible for clinical practice.</p><p><strong>Conclusions: </strong>Our findings underscore the importance of collaboration between users and technology designers in developing digital health technologies. Engaging the users helped identify technical and organizational constraints that needed to be addressed to integrate the digital framework into clinical settings.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e69679"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334038","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":"Feasibility and Effectiveness of an Urgent Care-Community Partnership to Reduce Disparities in Patient Portal Uptake: Quality Improvement Project.","authors":"Mechelle Sanders, Amaya Sanders, Erik Herbert, Naomi Rosie Booker, Sandy Wang, Kevin Fiscella","doi":"10.2196/69253","DOIUrl":"10.2196/69253","url":null,"abstract":"<p><strong>Background: </strong>Patient portals demonstrate significant potential for improving health care engagement but face critical adoption challenges. Disparities persist across different demographic groups, creating a digital divide in health care access. Targeted training strategies, particularly personalized and one-on-one approaches, show promise in increasing portal utilization. Innovative solutions, like community health workers specializing in digital navigation, offer a potential pathway to reduce enrollment barriers. The key challenge remains developing a scalable, cost-effective training model.</p><p><strong>Objective: </strong>Our quality improvement (QI) project aimed to assess the feasibility and effectiveness of a collaborative effort between a free community-based digital navigation program and an urgent care clinic in facilitating patient access to their portal.</p><p><strong>Methods: </strong>We created the Digital Health Equity Navigation Training (DHENT) program to improve patient portal access and usage. The program used a train-the-trainer model to scale up patient portal training across the community. DHENT trainers partnered with urgent care physicians to enroll patients in the portal. Physicians briefly explained portal benefits and referred interested patients for DHENT assistance. Trainers then contacted patients by phone to help with enrollment and navigation. We employed 3 Plan-Do-Study-Act cycles to understand the feasibility of the collaboration. We used descriptive statistics to describe participant characteristics and referral processes.</p><p><strong>Results: </strong>The collaboration was marginally successful, exceeding referral targets by 27.7% (115/90). Most patients were under 60 years old (94/115, 81.7%) and White (78/115, 67.8%). There was a significant delay in contact, averaging 37 days. While 4.8% (5/104) of patients accessed the portal with DHENT trainer assistance, 9.6% (10/104) had already signed up independently after their urgent care visit.</p><p><strong>Conclusions: </strong>Overall, we found our partnership had a moderate impact, and only a low dose of intervention and resources were needed.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e69253"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12161615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235444","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":"A Training Program to Support Patient Engagement in Primary Health Care Research: Co-Design, Implementation, and Evaluation Study.","authors":"Amanda L Terry, Lorraine Bayliss, Leslie Meredith, Eugene Law, Rob Van Hoorn, Sandra Regan","doi":"10.2196/65485","DOIUrl":"10.2196/65485","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patient engagement in research represents an evolution in how new knowledge is being created. Individuals and teams seeking to conduct research in this way want to learn how to best approach this aspect. Specialized training is required to ensure that these individuals and groups have the knowledge and skills to engage with and accomplish these goals. We developed a training program, called Patient-Oriented Research Training & Learning - Primary Health Care (PORTL-PHC), to address this need.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The objective of this paper was to describe key learning needs and knowledge gaps regarding patient-oriented research in primary health care, as well as the design, implementation, and evaluation of the PORTL-PHC program.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;First, we completed a needs assessment to determine the learning needs of the program's target groups (including patient partners, policy makers, health care practitioners, and researchers). Second, building on the results of the needs assessment, the development and implementation of the program followed a series of iterative steps, including user testing of the program's content and format. Third, we conducted an evaluation with two components: (1) program registrants were asked to respond to questions as they progressed through the training content that explored what aspects of the content users found the most useful, suggestions for improvement, and any difficulties navigating the learning platform; and (2) program registrants were administered a questionnaire in three waves (January 2020, July 2020, and September 2021) 6 months after they had completed the program, that asked them to rate their gains in different areas of knowledge and skills regarding patient-oriented research on a 5-point Likert scale.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;There were 205 learners who participated in the program from January 2018 to January 2022. The target audience was reached with registrants from all groups; the majority of learners were from Canada (194/205, 95%). A total of 6 main areas of knowledge needs were identified from the needs assessment, and the program was iteratively developed and refined to address these needs and our learning objectives. Suggestions for improvement received from the first component of the evaluation were used to enhance and refine the program. Of the 88 learners who had completed the program at the time of the evaluation questionnaire administration, 28 responded to our request to complete an evaluation. The results indicate that PORTL-PHC increased knowledge of patient-oriented PHC research (overall mean score of 4.36, SD .56). Learners gained skills and knowledge in identifying patient priorities in PHC (mean 4.27, SD .63), understanding the methods of patient engagement (mean 4.32, SD .65), and skills for engagement in patient-oriented research (mean 4.41, SD .50). The majority of respondents (23/28, 82%) indicated that the","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e65485"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12161618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235443","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}