Journal of Medical Internet Research

General practitioners (GPs) play a pivotal role in a patient’s health care journey. However, demands on general practice, including complex patient management, workforce shortages, and health system fragmentation, have been shown to adversely impact the delivery of high-quality care and health outcomes. Integrated care models, particularly those that offer virtual care options, can support improved access to quality care and efficiency of health care delivery across metropolitan and rural areas. The SUSTAIN model of care was created to provide an accessible option for integrated care. It consists of centralized pediatricians supporting GPs in their practice through virtual coconsultations, virtual “lunch and learn” case discussions, and phone or email support. There is limited evaluation literature on integrated models of care being implemented in a primary care setting where the GP and family are face to face and the non-GP specialist is virtual. To address this gap, a comprehensive implementation evaluation of the SUSTAIN model of care was conducted.

In China, lung cancer remains a major public health concern and accounts for a substantial proportion of cancer-related deaths nationwide. However, limited research has examined public perceptions of lung cancer in the digital sphere, where health-related information is increasingly disseminated and accessed.

Dilated cardiomyopathy (DCM), characterized by ventricular dilation and systolic dysfunction, has a 10-year survival rate of 25%. The number of young and middle-aged patients with DCM is increasing, with progressive physical limitations and psychosocial distress substantially impairing quality of life. However, illness experiences in this population remain underexplored, particularly in non-Western contexts. Social media platforms such as Zhihu and Weibo offer a novel avenue for exploring patient narratives and peer support.

Cancer clinical trials are essential for advancing therapeutic innovations; however, patient enrollment remains a persistent challenge globally. Understanding the attitudes and willingness of patients with cancer to participate in clinical trials is critical for improving recruitment strategies. While previous studies have explored barriers and facilitators, few have integrated multiple data sources or used emerging analytical approaches, such as large language models (LLMs), to capture the multidimensional nature of patient decision-making. Furthermore, limited research has examined these perspectives within the Chinese health care context, where cultural, economic, and systemic factors may uniquely influence participation decisions.

Artificial intelligence (AI) is increasingly proposed for use in health and health care systems. Beyond technical performance, public perceptions and affective responses influence whether AI technologies are accepted and adopted in real-world contexts. Social media platforms such as X (formerly Twitter) provide large-scale, real-time insight into public discourse surrounding emerging technologies, yet remain underused for examining how health AI is discussed, evaluated, and emotionally framed.

Artificial intelligence (AI) integration in mobile health (mHealth) apps offers health care access opportunities in low-resource settings, yet opaque AI recommendations undermine trust and adoption. Existing explainable AI (XAI) frameworks, designed in Western contexts, fail to address the linguistic, cultural, and infrastructural realities of South Asian populations, creating barriers where users cannot understand AI recommendations, clinicians cannot validate outputs, and developers lack implementation guidance. Thus, understanding explainability requirements among educated, digitally literate populations provides foundational insights for future development of inclusive mHealth technologies.

Digital health interventions (DHIs) often struggle with participant engagement. A stepped care approach, starting with low-resource intensity strategies and escalating as needed, can optimize resource use. Yet its application and cost implications remain underexplored.

The translation of big data analytics and artificial intelligence (AI) into clinical decision support systems (CDSSs) has advanced from proof of concept to real-world clinical practice. AI-informed CDSSs show measurable improvements in diagnostic accuracy, risk stratification, resource use, and patient outcomes compared to traditional models, offering the potential to assist clinicians in managing symptom complexity and uncertainty in health care delivery. Despite this potential, access to large amounts of high-quality and granular data remains one of the most significant bottlenecks to AI-enabled CDSSs. We argue that as health care systems increasingly adopt data-driven decision support, addressing the challenges of data accessibility and protection is essential to realizing the full potential of AI in clinical medicine. We use selected case examples of AI-informed CDSSs in oncology, organ transplantation, diabetic retinopathy, epilepsy, spinal cord injury, rare disease diagnosis, and emergency medicine to illustrate opportunities and challenges related to AI’s potential to improve patient outcomes. We discuss public and semipublic, medical institutional and commercial, and government and national data sources that are currently available for the development of CDSSs and highlight the practical and ethical constraints associated with these data. We consider alternative data resources and ways in which health care systems can strengthen data ecosystems to increase AI-driven CDSS efficacy and implementation to improve patient outcomes.

Medical residency is a demanding training stage characterized by high levels of stress and burnout. As digital natives, current medical trainees (ie, residents) are frequent users of social media; however, little is known about how their personal (nonprofessional) use relates to burnout and social media addiction (SMA).

Obesity is a major global health concern, and scalable digital solutions are urgently needed. While digital lifestyle interventions (DLSIs) have shown promise, prior meta-analyses often included hybrid formats with human support, limiting insights into the effectiveness of fully digital interventions.

Low physical activity (PA) is a well-established risk factor for major depressive disorder (MDD). However, the temporal dynamics of PA preceding an incident clinical diagnosis of MDD remain poorly characterized, particularly using long-term, objective measures collected in real-world settings.

Menstruation has long been framed primarily as a hygiene issue, with mainstream products and public messaging emphasizing concealment and disposal of menstrual blood (MB). This has contributed to a culture of silence in which conversations about menstrual health have been marginalized in public and clinical settings. Recent international guidance, including the World Health Organization’s call to reframe menstruation as a health issue, underscores the need for more open discourse. Simultaneously, social media has become a prominent space where menstruating individuals share experiences, seek advice, and challenge stigma. The resurgence of reusable menstrual products has increased users’ direct observation of MB, prompting questions about variations in color, texture, and smell. These developments highlight growing curiosity about MB yet reveal persistent information gaps regarding how MB is understood outside the clinical setting.