Journal of Medical Internet Research

Healthcare systems are increasingly facing challenges posed by the aging of populations. In particular hospitalization, both initial and subsequent, which is often observed among elderly patients. Yet, research suggests that nearly 23% of all hospitalizations could be avoided. In this perspective, remote patient monitoring (RPM) systems are emerging as a promising solution, enabling professionals to detect and manage patient complexities early within home-based care settings.

Clinical problem-solving requires processing of semantic medical knowledge such as illness scripts and numerical medical knowledge of diagnostic tests for evidence-based decision-making. As large language models (LLMs) show promising results in many aspects of language-based clinical practice, their ability to generate non-language evidence-based answers to clinical questions is inherently limited by tokenization.

The recent increase in online health information-seeking prompted extensive user appraisal of encountered content. Information consumption depends crucially on the quality of encountered information and the user’s ability to evaluate it; yet, within the context of online, organic search behavior, few studies take into account both these aspects simultaneously.

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including medical question-answering (QA). However, individual LLMs often exhibit varying performance across different medical QA datasets. We benchmarked individual zero-shot LLMs (GPT-4, Llama2-13B, Vicuna-13B, MedLlama-13B, and MedAlpaca-13B) to assess their baseline performance. Within benchmark, GPT-4 achieves best 71% on MedMCQA, Vicuna-13B achieves 89.5% on PubMedQA, and MedAlpaca-13B achieves best 70% among all, showing the potential for better performance across different tasks and highlighting the need for strategies that can harness their collective strengths. Ensemble learning methods, combining multiple models to improve overall accuracy and reliability, offer a promising approach to address this challenge.

Sixty percent of patients with opioid use disorder (OUD) leave treatment early. Psychosocial interventions can enhance treatment retention by addressing behavioral and mental health needs related to early treatment discontinuation, but engagement is key. If well-designed, digital platforms can increase the engagement, reach, and accessibility of psychosocial interventions. Prior reviews of opioid use disorder (OUD) treatment have predominantly focused on outcomes, such as reductions in substance use, without identifying the underlying behavior change principles that drive the effectiveness of interventions.

Inconsistencies in survey-based (eg, questionnaire) data collection across biomedical, clinical, behavioral, and social sciences pose challenges to research reproducibility. ReproSchema is an ecosystem that standardizes survey design and facilitates reproducible data collection through a schema-centric framework, a library of reusable assessments, and computational tools for validation and conversion. Unlike conventional survey platforms that primarily offer graphical user interface–based survey creation, ReproSchema provides a structured, modular approach for defining and managing survey components, enabling interoperability and adaptability across diverse research settings.

Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI success is crucial for enhancing patient outcomes and optimizing procedural strategies.

Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitable algorithms to support their work.

The increased integration of telehealth services into healthcare systems, especially during the COVID-19 pandemic, transformed patient-provider interactions. Despite numerous benefits that promote health equity and resource allocation, patients’ acceptance and use of telehealth has declined post-pandemic. To enhance healthcare delivery and patient satisfaction, we study the factors of this decline from the perspective of patient characteristics that influence the adoption and utilization of telehealth services.

The United States is facing an opioid overdose epidemic resulting in an unprecedented number of preventable deaths. The use of medications including buprenorphine and methadone have proven effective for opioid use disorder (OUD), but many patients struggle to stay in treatment. Novel solutions, such as digital health tools, offer one option to help improve clinic management and improve treatment engagement.

One of the main challenges with COVID-19 has been that although there are known factors associated with a worse prognosis, clinicians have been unable to predict which patients, with similar risk factors, will die or require intensive care unit (ICU) care.

With the continuous advancement of medical technology and the frequent occurrence of disaster events, the training of healthcare workers in disaster nursing has become increasingly significant. However, traditional training methods often struggle to engage learners' interest and enthusiasm, making it challenging to simulate emergencies in real-life scenarios effectively. Gamification, as an innovative pedagogical approach that enhances the enjoyment and practicality of learning through the incorporation of game elements, has garnered considerable attention in the realm of disaster nursing education for healthcare workers in recent years. This review systematically evaluates its effectiveness and explores its advantages in improving training outcomes.