Journal of Medical Internet Research

The German Health Data Utilization Act and the Digital Act aim to enhance health data sharing for health care and research in Germany and beyond while ensuring robust data protection. A key prerequisite is patients’ willingness to share their data for primary use (PU), such as medical care, and secondary use (SU), such as research. There is a lack of qualitative research examining patients’ perspectives on data sharing under the new legal framework, especially among vulnerable groups, such as those with mental health diseases.

Assessing Circumstances and Offering Resources for Needs (ACORN) is a US Department of Veterans Affairs (VA) clinical intervention designed to identify and address social needs to improve health and well-being among all veterans. We co-designed the ACORN Dashboard to facilitate access to real-time social needs and intervention data for VA clinical care teams and leadership.

Group chats on platforms such as WhatsApp (Meta Platforms, Inc), WeChat (Tencent Holdings Limited), and Telegram (Telegram FZ-LLC) are central to everyday communication in many settings, including across low- and middle-income countries and among groups often overlooked by one-to-one or app-based digital health tools. Yet their roles and underlying mechanisms as intentionally designed health interventions have not been comprehensively examined.

Digital health tools integrating electronic patient-reported outcome and experience measures (ePROMs/ePREMs) enable longitudinal monitoring of health-related quality of life (HRQoL), psychological well-being, and treatment satisfaction in pre-exposure prophylaxis (PrEP) users. However, determinants of sustained engagement with digital follow-up platforms remain insufficiently characterized.

This commentary reviews the study by Jones et al, which evaluated whether GPT-4 could improve the readability of injectable medication guidelines while preserving important safety information. The study found that GPT-4 produced modest readability gains comparable to manual revision, but also introduced omissions and meaning changes in a minority of sections. These findings highlight both the potential and limitations of early large language models (LLMs) in clinical contexts. However, this study reflects the capabilities of a specific model in a rapidly evolving domain. Since the release of GPT-4, advances in multistep reasoning, model-critique workflows, and structured validation have substantially improved the ability of newer systems to detect omissions, maintain factual fidelity, and support controlled editing. As a result, some documented limitations may stem from the constraints of a single-model, single-pass workflow rather than intrinsic flaws in LLM-assisted guideline revision. This commentary highlights the need for evaluation frameworks that can keep pace with LLM progress and emphasizes that clinical oversight and user-centered testing remain essential. Updated research using contemporary models is needed to determine how emerging architectures can more safely support clarity, consistency, and maintenance of clinical guidelines.

Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters.

Digital sexually transmitted and blood-borne infection (STBBIs) testing services are used to improve testing access, but might replicate existing social inequities. Previous research has shown that the digital STBBI testing service has improved access to testing in British Columbia (BC), Canada. As part of the program’s continuous evaluation, we examined awareness and use of the service in 5 urban, suburban, and rural communities where the program has expanded.

Automated structuring of radiology reports is essential for data utilization and the development of medical artificial intelligence models. However, manual annotation by experts is labor-intensive, and processing real clinical data through commercial large language models (LLMs) presents significant privacy risks. These challenges are particularly pronounced for non-English languages like Japanese, where specialized medical corpora are scarce. While synthetic data generation offers a potential privacy-preserving alternative, its effectiveness in capturing complex clinical nuances—such as negation and contextual dependencies—to train robust classification models without any real-world training data has not been fully established.

Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals’ views, but very few studies have examined the perspectives of people with psychosis on this topic.

The World Health Organization recommends that countries routinely collect data on the behavioral and social drivers (BeSD) of vaccination to inform public health interventions that increase vaccine uptake. There is a need to identify data collection methods that can rapidly and inexpensively collect representative data, particularly in low- and middle-income countries.

The rapid expansion of rehabilitation needs in China has intensified pressure on a workforce that remains unevenly distributed. Digital health technologies (DHTs) offer potential to increase service reach and efficiency. However, little is known about how rehabilitation professionals currently gather and document clinical information, nor about their readiness to integrate digital tools into routine practice within China’s rapidly digitalizing health system.