Journal of Participatory Medicine最新文献

{"title":"Engaging Young People With Mental Health Needs and Exploring Outputs From a Resource Development Project: Qualitative Interview Study.","authors":"Zoë Haime, Charlotte Carney, Myles-Jay Linton, Helen Bould, Lucy Biddle","doi":"10.2196/74258","DOIUrl":"10.2196/74258","url":null,"abstract":"<p><strong>Background: </strong>Recommendations from professional bodies, including the Royal College of Psychiatrists, advise mental health practitioners to discuss problematic online use with children and young people. However, barriers such as knowledge gaps and low confidence in initiating discussions often prevent these conversations from happening.</p><p><strong>Objective: </strong>The Digital Dialogues project used a knowledge exchange approach, cocreating resources with young people, to support professionals in overcoming these challenges. This paper details the project design and reflects on the perspectives of the young people involved.</p><p><strong>Methods: </strong>The project was guided by the \"children and young people have ownership\" model of cocreation. A total of 11 participants were purposively sampled to take part in the Digital Dialogues Young Persons Group (DDYPG) and were actively involved in the study workshops, creative tasks, and resource design and development. In total, 6 (55%) DDYPG members took part in interviews, and 2 (18%) also completed an anonymous survey evaluating their time in the DDYPG. Thematic analysis was used to explore data from interviews and qualitative survey responses together.</p><p><strong>Results: </strong>The DDYPG successfully created several resources to support practitioners in addressing problematic online use with young people. Reflections from DDYPG members showed that creative engagement, meaningful involvement, and peer interactions were key motivators for participation and led to benefits, including feelings of empowerment and personal development. Anxiety, time demands, and potential exposure to triggering content could act as barriers. However, structured tasks, positive rapport with researchers, and flexible participation helped to mitigate these challenges.</p><p><strong>Conclusions: </strong>The findings highlight ethical considerations and potential strategies for involving young people in resource development research projects in the future.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e74258"},"PeriodicalIF":0.0,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972529","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":"Engagement Methods in Brain Tumor Genomic Research: Multimethod Comparative Study.","authors":"Matthew DeCamp, Juliana G Barnard, Carly Ritger, Laura J Helmkamp, Anowara Begum, Sandra Garcia-Hernandez, Rudy Fischmann, Nestelynn Gay, Ricardo Gonzalez-Fisher, Kevin C Johnson, Lindsay A Lennox, Guy R Lipof, Jasmyn Ostmeyer, Ifeoma Perkins, Laura Pyle, Liz Salmi, Talia Thompson, Elizabeth B Claus, Roel Verhaak, Bethany M Kwan","doi":"10.2196/68852","DOIUrl":"10.2196/68852","url":null,"abstract":"<p><strong>Background: </strong>Engaging patients, care partners, and others in research planning and conduct is increasingly valued. However, identifying the most effective ways to do so remains a challenge.</p><p><strong>Objective: </strong>This study aimed to evaluate participation and participant experience using 3 engagement methods with the Low-Grade Glioma (LGG) Registry's Optimizing Engagement in Discovery of Molecular Evolution of Low-Grade Glioma (OPTIMUM) project, part of the National Cancer Institute's Participant Engagement and Cancer Genome Sequencing Network.</p><p><strong>Methods: </strong>We evaluated LGG Registry research advisory council (RAC) meetings, Twitter (now known as X), and Facebook discussions across 4 engagement activities with each group. Researchers recorded discussions and performed qualitative content analysis to evaluate differences in the nature of interactions and recommendations for promoting trust and participation in LGG Registry research. Participants completed experience surveys after engagements 1 and 4 (Public and Patient Engagement Evaluation Tool, Research Engagement Survey Tool, Trust in Medical Researchers Scale, and Patient Engagement in Research Scale).</p><p><strong>Results: </strong>RAC engagements involved 25 unique participants representing diverse backgrounds; tweet chats and Facebook discussions had 197 and 133 participants, respectively. Qualitative findings highlighted differences in the nature of interactions (eg, communication styles and types of information shared) across groups, but there was general agreement around recommendations for promoting participation in genomic research. Postengagement surveys (n=52 in ipostengagement activity 1; n=40 in postengagement activity 4) showed patterns suggesting a more positive experience overall for the RAC.</p><p><strong>Conclusions: </strong>Advisory councils and social media engagement methods have advantages and disadvantages. Advisory councils provide consistent interactions with the same individuals and clear procedures. Despite theoretically broader reach, social media engagement may yield less diverse perspectives. The LGG Registry aims to use RAC and social media engagement methods to promote diverse perspectives and maintain consistent interactions.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e68852"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972499","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":"Perception of AI Use in Youth Mental Health Services: Qualitative Study.","authors":"Xiaoxu Ding, Skye Barbic","doi":"10.2196/69449","DOIUrl":"10.2196/69449","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) technology has made significant advancements in health care. A key application of using artificial intelligence for health (AIH) is the use of AI-powered chatbots; however, empirical evidence on their effectiveness and feasibility remains limited.</p><p><strong>Objective: </strong>This study explored interest group perceptions of integrating AIH in youth mental health services, focusing on its potential benefits, challenges, usefulness, and regulatory implications.</p><p><strong>Methods: </strong>This qualitative study used semistructured in-depth interviews with 23 mobile health stakeholders, including youth users, service providers, and nonclinical staff from an integrated youths' service network. We used an inductive approach and thematic analysis to identify and summarize common themes and subthemes.</p><p><strong>Results: </strong>Participants identified AIH's potential to support education, navigation, and administrative tasks in health care, as well as to create safe spaces and mitigate health resource burdens. However, they expressed concerns about the lack of human elements, such as empathy and clinical judgment. Key challenges included privacy issues, unknown risks from rapid technological advancements, and insufficient crisis management for sensitive mental health cases. Participants viewed AIH's ability to mimic human behavior as a critical quality standard and emphasized the need for a robust evaluation framework combining objective metrics with subjective insights.</p><p><strong>Conclusions: </strong>While AIH has the potential to improve health care access and experience, it cannot address all mental health challenges and may exacerbate existing issues. While AIH could complement less-complex services, it could not replace the therapeutic value of human interaction at this time. Co-design with end users is critical for successful AI integration. Robust evaluation frameworks and an iterative approach to build a learning health system are essential to refine AIH and ensure it aligns with real-world evolving needs.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e69449"},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144883928","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":"Generative AI as Third Agent: Large Language Models and the Transformation of the Clinician-Patient Relationship.","authors":"Hugo de O Campos, Daniel Wolfe, Hongzhou Luan, Ida Sim","doi":"10.2196/68146","DOIUrl":"10.2196/68146","url":null,"abstract":"<p><strong>Unlabelled: </strong>The use of artificial intelligence (AI) in health care has significant implications for patient-clinician interactions. Practical and ethical challenges have emerged with the adoption of large language models (LLMs) that respond to prompts from clinicians, patients, and caregivers. With an emphasis on patient experience, this paper examines the potential of LLMs to act as facilitators, interrupters, or both in patient-clinician relationships. Drawing on our experiences as patient advocates, computer scientists, and physician informaticists working to improve data exchange and patient experience, we examine how LLMs might enhance patient engagement, support triage, and inform clinical decision-making. While affirming LLMs as a tool enabling the rise of the \"AI patient,\" we also explore concerns surrounding data privacy, algorithmic bias, moral injury, and the erosion of human connection. To help navigate these tensions, we outline a conceptual framework that anticipates the role and impact of LLMs in patient-clinician dynamics and propose key areas for future inquiry. Realizing the potential of LLMs requires careful consideration of which aspects of the patient-clinician relationship must remain distinctly human and why, even when LLMs offer plausible substitutes. This inquiry should draw on ethics and philosophy, aligned with AI imperatives such as patient-centered design and transparency, and shaped through collaboration between technologists, health care providers, and patient communities.</p>","PeriodicalId":36208,"journal":{"name":"Journal of Participatory Medicine","volume":"17 ","pages":"e68146"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875608","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":"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}