Journal of Medical Internet Research

Virtual health care models, such as (HaH), are an alternative to in-person care and allow hospitals to expand care capacity and delivery without the need for additional brick-and-mortar structures. While generally well received, there is an overall lack of awareness among those receiving and giving care about what HaH is and what it does, and uncertainty about the conditions needed to implement HaH in a safe, sustainable, and equitable way.

Artificial intelligence (AI)–generated lifestyle recommendations are increasingly used to support health behavior change. However, AI advice does not necessarily mean that users will accept or adopt those recommendations. Although prior reviews have examined AI-enabled lifestyle interventions and health behavior technologies, fewer have focused on whether users accept and adopt AI-generated recommendations.

Rapid technological advances have led to the development of generative artificial intelligence (GenAI). GenAI tools such as ChatGPT (OpenAI) and DALL-E (OpenAI) can generate text and images in response to prompts and have permeated various life domains. Existing literature has reported mixed relationships between GenAI use and life satisfaction.

Generative artificial intelligence (AI) has the potential to impact health care by transforming workflows and improving outcomes. Patient-centered clinical decision support (PC CDS) are digital tools that use patient-specific information and patient-centered outcomes research to improve health care decision-making. Generative AI is increasingly being incorporated into PC CDS tools. As patient-facing digital tools continue to expand within the health ecosystem, it is important to gather patient and caregiver perspectives about engaging with generative AI–supported PC CDS tools.

Interdepartmental consultations are essential for managing complex inpatient care but are often inefficient. Hospital-wide, data-driven analyses are needed to guide process improvements; yet, most existing studies have focused on single departments or specific diseases, leaving a gap in understanding hospital-level collaboration networks. Understanding these patterns is crucial for optimizing clinical workflows, reducing delays, and improving patient outcomes in large tertiary hospitals.

Large language models (LLMs) show growing potential for decision support. However, integrating domain-specific medical knowledge while maintaining accuracy, safety, and interpretability remains challenging for postoperative discharge instructions and patient education. Fine-tuning, retrieval-augmented generation (RAG), and hybrid fine-tuning+RAG approaches are prominent strategies for knowledge integration, but their comparative performance in postoperative care has not been systematically evaluated.

As artificial intelligence (AI) chatbots become an increasingly common source of quick medical guidance, it is important to understand whether their responses meet users’ needs and support well-informed health decisions. Yet, existing evaluation frameworks rely primarily on expert-defined evaluation dimensions that have not been empirically validated with end users. It remains unclear whether these frameworks capture the criteria people actually use when judging a response to be useful, trustworthy, and satisfying, or which evaluation dimensions matter most to users in practice.

No preview text available.

Patient-reported outcomes in digital health solutions can offer patients with type 1 diabetes an opportunity to voice their needs in outpatient care, enabling clinicians to tailor support. Evidence on long-term health impact and routine integration of such digital solutions outside controlled settings is limited.

Problem-solving is essential for the self-management of type 2 diabetes but remains challenging for underserved individuals. Although mobile health (mHealth) interventions can improve diabetes self-management, few focus on problem-solving.