Journal of Participatory Medicine最新文献

Unlabelled: Advancements in diagnostic technologies for the evaluation of infectious disease complaints in the outpatient setting have improved the speed and accuracy of pathogen detection and created the opportunity for higher accuracy in treatment planning. The benefits of these advanced diagnostics insights can be optimized when coupled with robust shared decision-making between the patient and clinician during the clinical encounter. This manuscript describes the process for the integration of results from molecular testing for respiratory tract infection into a shared decision-making framework. It also explores how this synergy may lead to improved patient outcomes, enhanced health care delivery, and more collaborative care, while enhancing diagnosis and treatment of respiratory infections in various clinical settings.

Background: Dementia poses a significant challenge in India. The rising incidence rates, limited resources, and restricted clinician access have contributed to a staggering 90% gap in diagnosis and care. Conversational technology provides a natural user interface with the potential to promote the independence, well-being, and safety of people living with dementia at home. However, the feasibility of implementing such technology to support people living with dementia across diverse cultural and economic settings remains underexplored.

Objective: This study aimed to assess the cultural feasibility of conversational robots for dementia care in India, a culturally underserved context in robotics and artificial intelligence (AI) for aging and dementia care.

Methods: We involved 29 stakeholders, including people living with dementia, caregivers, and dementia care professionals. We evaluated (1) the engagement of people living with dementia with 3 conversational robots with varying interactive modalities (a voice agent, a virtual affective robot, and an embodied robot), (2) robot acceptance, and (3) stakeholder perspectives on the benefits and challenges of deploying conversational AI in India.

Results: People living with dementia were willing to engage in verbal dialogue with conversational robots. Stakeholders perceived the technology as beneficial for supporting daily tasks at home, reducing loneliness, and enhancing cognitive function. We identified design adaptations to address feasibility challenges in India, including the need to (1) adapt interaction style to use a kind tone, appreciative language, and customizable facial expressions; (2) improve speech recognition for local accents interpretation and noisy settings; and (3) introduce prototypes in local clinics to promote familiarity.

Conclusions: This work offers novel insights into cultural acceptance, human-robot engagement, and perceived utility for dementia care, along with key design implications for integrating conversational AI into care settings in India.

Background: The prevalence and incidence of dementia are higher in migrants and those with low socioeconomic status (SES). Mobile health (mHealth) interventions offer a potentially scalable way to reduce dementia risk via risk factor modification.

Objective: We co-designed the MIND-PRO app-an mHealth intervention targeting dementia risk factors through self-managed lifestyle changes and remote coaching-specifically designed for Dutch individuals with low SES and those with Turkish or South Asian Surinamese migration backgrounds. We focused on these migrant populations as they are the largest in the Netherlands and have the highest risk of developing dementia.

Methods: In this qualitative study, we explored the needs and preferences of our target populations aged 50-75 years old at increased dementia risk by conducting semistructured interviews and focus groups. Participant feedback was used to iteratively refine and adapt a prototype intervention based on insights from prior mHealth trials.

Results: We interviewed 23 participants (median age 59, IQR 55-63 y; n=15, 65% female) and conducted two focus groups with 7 Turkish women and 13 Dutch participants with low SES. The target populations emphasized personalization features such as goal setting, self-tracking, educational material, and remote coaching. Participants highlighted the importance of social interaction and autonomy in achieving sustainable lifestyle changes. Tailoring coaching and lifestyle advice to cultural practices was deemed beneficial.

Conclusions: Optimal mHealth interventions targeting dementia risk factors in migrants and individuals with low SES should be personalized and interactive, respect autonomy, and integrate cultural needs and preferences.

Background: Communication of imaging results is increasingly directed to patients, but controversies remain regarding report communication from the perspectives of patients, ordering providers, and radiologists.

Objective: The objective of this study was to compare and contrast patients and providers with respect to their preferred source of imaging report information, preferred method of imaging report communication, and perceptions of patients' understanding of imaging reports.

Methods: We gathered preferences from patients and providers through surveys. In total, 91 patients as well as 77 physicians, 10 physician assistants, 6 nurse practitioners, and 1 other health provider completed the surveys. Chi-square and 2-tailed t tests were used to compare differences in means between the groups. Logistic regression was used to analyze the probability of an ordering provider preferring online release of imaging results as the first method of communication as a function of provider characteristics.

Results: Of the 94 providers who participated in the study, 53 (56%) were women and 80 (85%) were White. On average, they had 15.6 (SD 10) years of experience. Most providers preferred delaying the release of imaging reports to patients until after they had reviewed the report themselves. There was substantial provider preference heterogeneity regarding imaging report communication to patients and the timing of release. The majority of the patients (70/91, 77%) who completed the survey were women, and 19% (17/91) identified as members of racial and ethnic minoritized groups. Patients generally preferred to receive their imaging results online as soon as they were available.

Conclusions: The findings of this study suggest that shared decision-making between patients and providers before the release of imaging results could help establish how, when, and by whom results should be delivered to patients. The study findings can be leveraged to explore options for a differentiated reporting approach that is more responsive to patient and provider needs.

Background: Preventing diabetes is a priority for governments and health systems worldwide. Artificial intelligence (AI) has the potential to inform prevention and planning. However, there is little guidance on how patients, caregivers, and communities are engaged in AI life cycle stages.

Objective: This formative qualitative study aimed to identify principles for meaningful community engagement. The goal was to support the responsible use of machine learning models in diabetes prevention and management.

Methods: We conducted a literature scan on how AI or digital health initiatives have engaged patients and communities. A participatory workshop was then organized with patients, caregivers, community organizations, clinicians, and policymakers. In the workshop, we identified and ranked guiding principles for community engagement in AI for population health. We also outlined key considerations for implementing these principles.

Results: We identified 10 principles for patient and community engagement in AI for health care from 6 papers and developed a conceptual framework for community engagement on AI. A total of 30 workshop participants discussed and ranked the top 6 principles: trust, equity, accountability, transparency, codesign, and value alignment. Participants noted that embedding community engagement in the AI life cycle requires inclusivity and diversity. Additionally, implementers should leverage existing resources and adopt a centralized approach to AI decision-making.

Conclusions: Our study offers useful insights for community-focused AI deployment that centers the values of patients and communities. The identified principles can guide meaningful engagement on the use of AI in health systems, while future research can operationalize the conceptual framework.

Background: Patient engagement in research is the meaningful and active involvement of patient and caregiver partners (ie, patients and their family or friends) in research priority-setting, conduct, and governance. With the proper support, patient and caregiver partners can inform every stage of the research cycle, but common barriers often prevent their full engagement.

Objective: This participatory qualitative study aimed to answer the question: What are the facilitators and barriers to patient engagement experienced by patient and caregiver partners in a Canadian research context?

Methods: Participants were N=13 patient and caregiver partners (median age 62 y, IQR 58-69 y; 11/13, 85% women; 13/13, 100% White) from 4 provinces who completed 60-90-minute semistructured videoconferencing interviews. The interviews were transcribed verbatim. A researcher and a patient partner reviewed the transcripts and curated a dataset of 90 participant quotations representing facilitators and barriers to patient engagement. This dataset was co-analyzed using participatory theme elicitation alongside 7 patient and caregiver partners with diverse identities who were not among the participants we interviewed and, therefore, contributed novel perspectives.

Results: We generated four themes depicting factors that facilitate meaningful patient engagement alongside barriers that arise when these factors are not in place: (1) Co-defining roles and expectations; (2) demonstrating the value and impact of engagement; (3) psychological safety; and (4) community outreach, training, and education. We then discuss how barriers to enacting these 4 factors can be mitigated and provide a practical checklist of considerations for both researchers and patient and caregiver partners for engaging together throughout the research cycle.

Conclusions: Research teams conducting patient and caregiver engagement activities should draw from our findings to mitigate barriers and facilitate meaningful engagement experiences.

The adoption of artificial intelligence (AI) in health care has outpaced education of the clinical workforce on responsible use of AI in patient care. Although many policy statements advocate safe, ethical, and trustworthy AI, guidance on the use of health AI has rarely included patient perspectives. This gap leaves out a valuable source of information and guidance about what responsible AI means to patients. In this viewpoint coauthored by patients, students, and faculty, we discuss a novel approach to integrating patient perspectives in undergraduate premedical education in the United States that aims to foster an inclusive and patient-centered future of AI in health care.

Background: Public deliberation is a qualitative research method that has successfully been used to solicit laypeople's perspectives on health ethics topics, but it remains unclear whether this traditionally in-person method can be translated to the online context. The MindKind Study conducted public deliberation sessions to gauge the concerns and aspirations of young people in India, South Africa, and the United Kingdom with regard to a prospective mental health databank. This paper details our adaptations to and evaluation of the public deliberation method in an online context, especially in the presence of a digital divide.

Objective: The purpose of this study was to assess the quality of online public deliberation and share emerging learnings in a remote, disseminated qualitative research context.

Methods: We convened 2-hour structured deliberation sessions over an online video conferencing platform (Zoom). We provided participants with multimedia informational materials describing different ways to manage mental health data. We analyzed the quality of online public deliberation in variable resource settings on the basis of (1) equal participation, (2) respect for the opinions of others, (3) adoption of a societal perspective, and (4) reasoned justification of ideas. To assess the depth of comprehension of the informational materials, we used qualitative data that pertained directly to the materials provided.

Results: The sessions were broadly of high quality. Some sessions were affected by an unstable internet connection and subsequent multimodal participation, complicating our ability to perform a quality assessment. English-speaking participants displayed a deep understanding of complex informational materials. We found that participants were particularly sensitive to linguistic and semiotic choices in the informational materials. A more fundamental barrier to understanding was encountered by participants who used materials translated from English.

Conclusions: Although online public deliberation may have quality outcomes similar to those of in-person public deliberation, researchers who use remote methods should plan for technological and linguistic barriers when working with a multinational population. Our recommendations to researchers include budgetary planning, logistical considerations, and ensuring participants' psychological safety.