Journal of Participatory Medicine

Research projects conducted in collaboration with patients and caregivers are more rigorous, relevant, and impactful within the communities of focus. One mechanism for facilitating patient and caregiver engagement in research is through a lived experience panel (LEP)—an intentionally assembled group of people with personal experience relevant to the research topic. LEPs can engage in any or all aspects of the research project, from study ideation to participant recruitment to dissemination and translation of findings. Fully realizing the potential of an LEP to inform the research process requires intentionality in LEP composition, as members should align with the range of lived experiences in the community of focus. Mechanisms to recruit patient or caregiver advisors for research may require researchers to reach beyond pre-existing relationships if they intend to achieve maximum variability. However, explicit processes for forming a maximally variable LEP are scarce. In this guide, we outline an LEP formation process that includes laying the foundation for recruitment with academic and community partners; collaboratively developing promotional materials and an interest survey; using community-embedded promotional channels to share information about the LEP opportunity; and selecting interviewees and LEP invitees using a process called maximum variance sampling. Outcomes of our LEP formation process include the relative productivity of each promotional channel, the specific timeline from LEP ideation to formation, the sociodemographic variability of our applicants, and real LEP members’ feedback on the formation process. We conclude by highlighting what we perceive as keys to success, including collaboration with trusted community partners and a thoughtfully designed interest survey that enabled us to narrow a large applicant pool to a diverse set of invitees. We also describe challenges and lessons learned, including opportunities to better articulate LEP responsibilities during interviews and ways to navigate the tensions inherent in selecting members based on maximum variance. Researchers are encouraged to invest time and resources into building relationships with community partners, knowing that these relationships lay the groundwork for reaching diverse patients and caregivers to advise research.

Health digital twins, computational models that integrate longitudinal data, simulation, and forecasting, are increasingly proposed as tools for chronic care management. However, most current implementations are expert oriented, prioritizing technical optimization and clinical prediction while offering limited support for patient understanding, engagement, or participation. This orientation is particularly misaligned with chronic care, which unfolds largely outside clinical settings and depends on patients’ daily decisions, social context, and sustained engagement over time. In this viewpoint, we argue for reframing digital twins as participatory systems that support shared sensemaking among patients, caregivers, and clinicians rather than functioning solely as directive, expert-facing tools. We propose a conceptual framework that positions participatory digital twins as boundary objects capable of bridging computational models, clinical reasoning, and lived experience. Within this framework, generative artificial intelligence serves as a translation and interaction layer, enabling plain-language dialogue, exploration of uncertainty, and “what-if” reasoning that allows users to interpret model outputs in relation to their own contexts, goals, and constraints. We outline key design principles for participatory digital twins, including visible uncertainty, negotiated rather than prescriptive care, mechanisms for incorporating patient context and social drivers of health, and governance structures that support accountability and recourse. At the same time, this approach depends on meaningful opportunities for participation; appropriate safeguards around generative interaction; and careful attention to privacy, consent, and uneven access to digital resources. By shifting the focus from optimization alone to understanding, interaction, and trust, participatory digital twins offer a pathway toward more equitable, human-centered, and sustainable models of artificial intelligence–enabled chronic care.

A new approach has been developed to establish the public value (utility) set for the generic health measure used in quality-adjusted life year estimates. In contrast to conventional approaches, it establishes the complete utility set for an individual and aggregates a sample of these to achieve the public set. The novel way of establishing the complete utility set for an individual has the potential to transform the nature and extent of a patient’s participation in the clinical decision-making process. We have modified the online elicitation of personal utility functions approach to overcome its impracticalities in a clinical consultation. The main modification is the replacement of choice-based items by scale-based ones, on the grounds that the former’s time and cognitive demands, while tolerable in the research context, make it infeasible in practice. The personal utility set for healthcare (PUSH) approach, like the online elicitation of personal utility functions one, may be used with any multidimension, multilevel instrument, including condition-specific ones, but the empirical application here is with the health-related quality of life instrument EQ-5D-5L. PUSH for EQ-5D-5L is a decision support tool in the form of a spreadsheet workbook. The clinician assists nondirectively in the elicitation of the patient’s utility set for EQ-5D-5L. Subsequently, the clinician, drawing on the best available evidence and information, enters the EQ-5D-5L states they judge, on the balance of probabilities, the patient will be in (at an agreed future time point), for specific interventions, plus no intervention. The relevant country’s public set utility for each displayed health state is simultaneously revealed. (Those for 13 countries are in the current template.) It is envisaged that the clinician holds the PUSH template on their computer and opens a new copy for use with each patient. They agree with the patient on what, if anything, is to be saved as part of their electronic medical record. The deliberation following engagement with PUSH and personalized evaluation of the contemplated interventions will typically involve sensitivity testing and possible revision of the patient and clinician inputs. One key responsibility of the clinician is to dispel any “aura of exactness” or pseudo-precision that may be created by the use of precise percentages (or values to 2 decimal places). PUSH participation is to be seen as a component of deliberative co-productive decision-making to which both parties contribute significantly but in role-appropriate ways. The outputs are intended to provide a useful, analysis-framed input into the subsequent discussion and co-produced decision. As a major clinical innovation that transforms both patient participation and clinician contribution, it is advanced here for the discussion and critique that will enable a conceptually sound trial protocol to be developed (including clinician tutoring).

Antidepressant use and withdrawal are often accompanied by side effects such as dizziness, weight gain, and sexual dysfunction. Antidepressants and their associated side effects are stigmatized topics. Social media platforms such as Reddit are considered “safe spaces” by users because they can freely share their experiences and receive support.

The health of aging populations is among the top challenges facing global health systems. The use of mobile health (mHealth) approaches has been found to be effective in prompting changes and has been identified as a potentially valuable tool to enhance health behaviors among older adults.

Health care is entering an era of unprecedented detection. Artificial intelligence (AI)–driven monitors and real-world data streams now identify clinical risks in minutes, promising a future of proactive, earlier intervention. While AI automation is often marketed as a tool to reduce administrative burden and allow health care providers to focus more on direct patient care, this unrealized potential currently stands in contrast to our reality. Building high-speed data “freeways” without “off-ramps” such as clinical staffing, workflow synergy, and the patient education required for meaningful response is like building a superhighway without well-engineered off-ramps to provide a safe way to get home, and this creates a dangerous paradox. Earlier detection without earlier care does not improve outcomes; it simply redistributes anxiety and extends the patient’s period of uncertainty. We argue that the “public as a sensor” is already signaling a systemic infrastructure gap. True safety in clinical AI isn’t found in more algorithmic guardrails, but in participatory co-design that ensures every digital alert has a viable human pathway to care and resolution. We must stop building high-speed roads that lead to a cliff edge of clinical unavailability and consider that while the technology is a feat of engineering, it’s our human architecture that makes it medicine.

People living in rural areas of British Columbia experience inequities in access to health care that impact health and well-being. Since time immemorial, Indigenous peoples have had a holistic understanding of health and wellness, and knowledge of healthful ways of living. However, in a rural context, Indigenous peoples contend not only with inequitable access to health care, but also with the historical and ongoing impacts of colonization. Virtual health innovations enable access to care closer to home, yet the need for diagnostic tests and essential medicines remains limited by supply chains. In this context, transport of medical supplies by drone offers a promising solution and has the potential to improve access to health care in rural and First Nations communities in British Columbia.

Black, Indigenous, and people of color (BIPOC) experience disproportionately negative mental health outcomes, including rising suicide rates, and persistent barriers to culturally responsive care. Systemic racism, discrimination, and historical trauma contribute to mistrust of traditional mental health systems. Peer-led approaches that center lived experience and shared cultural identity may offer culturally responsive alternatives that foster emotional connection and trust.

Hispanic individuals face a higher risk of skin cancer due to disproportionate occupational sun exposure; yet, culturally tailored resources for this demographic are scarce.

Validated measurement tools that focus solely on assessing adolescents’ involvement in their treatment are scarce.

Schools can play an important role in supporting child weight management, with evidence suggesting that school-based interventions can be effective, albeit with substantial variation. Part of this variation could reflect the different contexts in which interventions are implemented. Our previous work found that co-production of research and adopting a complex systems model of evidence can be transformative for understanding this context. However, less is known about how co-production helps prioritize elements for exploration or how we should assess the value of co-production in research development. This study addresses these gaps by co-producing a refined logic model (a graphical representation of a theory) of the contextual influencers on school-based weight management to support public health research and policymaking.

The integration of patient input into drug research & development (R&D) enables the production of medicines that better meet the needs of patients. While momentum for advancing the science of patient input has continued to grow, there remain a host of barriers to full implementation and integration of systematic approaches for collecting and using robust and meaningful patient input data to inform decision-making. To help address these barriers, the Advancing the Science of Patient Input Action Collaborative (the collaborative), an activity associated with the National Academies of Sciences, Engineering, and Medicine (National Academies) Forum on Drug Discovery, Development, and Translation, organized a multi-stakeholder endeavor to identify and address key barriers to implementation through a series of information-gathering efforts. The collaborative engaged a wide range of perspectives to seek out practical paths forward to better align drug discovery, development, and regulation with patient priorities for disease management and treatment. Collaborative participants focused on three overarching research priorities which, if effectively addressed, would help advance the science of patient input: 1) understanding the patient experience over the course of a given disease or medical condition, 2) capturing the patient perspectives and priorities on benefit-risk, and 3) incorporating patient input into clinical trial design and continuous improvement. Addressing these research priorities would help decision makers shift away from using patient input in particular cases or for one-off applications and towards the integration of patient input as part of everyday medical research and practice. Building upon existing guidances and strategies, and sharing lessons learned from use cases, a comprehensive patient input framework would serve as a critical step towards reimagining and enriching the science of patient input throughout the drug R&D process, enabling a future in which medicines more fully meet the needs of patients.