Journal of Participatory Medicine

Chronic health conditions (CHC) are a recognized risk factor for the experience of problems in sexual function (PSF). Only a subset develops severe symptoms of sexual distress, the defining criterion for clinically relevant sexual dysfunction (SD) according to the ICD-11. Data on the contribution of specific CHC to clinically relevant SD symptoms and related healthcare needs are limited, hindering targeted interventions.

Launched in January 2022, the SingHealth Patient Advocacy Network @ Department of Medicine (SPAN@DEM) represents the first emergency department-specific advocacy group in Singapore. This initiative marks a significant advancement in local patient advocacy efforts because it employs a shared collaborative model to address the needs and concerns of patients within the unique context of the emergency department environment. SPAN@DEM emerged in recognition of the limitations of existing cluster-level advocacy groups, which are inadequate to address specific challenges inherent to the fast-paced, high-pressure nature of the emergency department.

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.

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.

Artificial intelligence (AI) technology has made significant advancements in healthcare. A key application of using AI for health (AIH) is the use of AI-powered chatbots; however, empirical evidence on their effectiveness and feasibility remains limited.

The use of artificial intelligence (AI) in healthcare 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, healthcare providers, and patient communities.

More than a few concepts have been presented in rehabilitation clinics that implement aspects of modern information technology 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 information technologies.

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 healthcare insights, AI must have access to comprehensive longitudinal health records (LHRs) that span clinical, genomic, non-clinical, wearable and patient generated data. Despite the extensive use of electronic medical records (EMR) and widespread interoperability efforts, current healthcare organizations, EMR 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.

Most definitions of therapeutic empathy are based on practitioners’ perspectives and few account for patients’ views. We therefore do not understand what therapeutic empathy means to patients. Given that therapeutic empathy involves a relationship between patients and practitioners, the under-representation of the patient voice threatens to undermine the validity of therapeutic empathy definitions and subsequently, how the concept is measured, taught, and practiced.

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 pertaining to their integration.

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.

Artificial intelligence (AI) Is rapidly transforming healthcare, 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.