JMIR Medical Informatics

Electronic health records (EHRs) can aid in provider efficiency, but may also lead to unintended consequences, such as documentation burden and increased length of notes. To combat issues related to documentation, copying and pasting (CP) and copying or carrying forward (CF) are tools that have been used to aid in documentation burden. Multiple studies have identified the benefits and challenges of using these tools; however, few studies have identified the unintended consequences of CP and CF, and how the adoption of these tools may affect users.

Accurately predicting the survival outcomes of patients with lung cancer receiving chemotherapy remains challenging.

Knee cartilage injury poses significant challenges in the early clinical diagnosis process, primarily due to its high incidence, the complexity of healing, and the limited sensitivity of initial imaging modalities.

We designed learning assignments for students to develop knowledge, skills, and professional attitudes about Generative AI (Gen AI) in two different master’s level courses in health informatics. Our innovative approach assumed students had no technical background or experience in using Gen AI tools.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a leading cause of chronic disease and can deteriorate into liver fibrosis or hepatocellular carcinoma. Each of its subtypes—obesity, diabetics, and lean—is associated with divergent fibrosis burdens and complications, yet existing analytics methods often overlook its multisystem nature, intra-phenotype heterogeneity, and disease dynamics. These limitations hinder accurate risk stratification and restrict personalized intervention planning.

Acute care utilization (ACU) represents a major economic burden in oncology, which can ideally be prevented. Existing models effectively predict such events.

Clinical trial eligibility screening using electronic medical records (EMRs) is challenging due to the complexity of patient data and the varied clinical terminologies. Manual screening is time-consuming, requires specialized knowledge, and can lead to inconsistent participant selection, potentially compromising patient safety and research outcomes. This is critical in time-sensitive conditions like acute ischemic stroke. While computerized clinical decision support tools offer solutions, most require software engineering expertise to update, limiting their practical utility when eligibility criteria change.

Efforts are being made to integrate digital health technologies into clinical care for multiple sclerosis (MS), to improve patient monitoring. Efficiently probing how they might impact clinical care could streamline and focus digital tool development. The Floodlight digital tool, comprising 5 smartphone sensor–based tests, was used to generate health-related data on patient function and symptoms in a clinical simulation.

The use of large language models (LLMs) in radiology is expanding rapidly, offering new possibilities in report generation, decision support, and workflow optimization. However, a comprehensive evaluation of their applications, performance, and limitations across the radiology domain remains limited. Objective: To map current applications of LLMs in radiology, evaluate their performance across key tasks, and identify prevailing limitations and directions for future research.

Liver failure often results in significant coagulation dysfunction, which is a major complication. Artificial liver support systems (ALSS) have been used to ameliorate coagulation parameters, but the dynamic nature of these improvements and the development of predictive models remain insufficiently explored.

This Author Reply addresses the comments raised in response to our Viewpoint, “Digital Health Innovations to Catalyze the Transition to Value-Based Health Care.” We appreciate the thoughtful reflections that emphasize the broader system-level requirements for implementing value-based health care (VBHC) beyond technology adoption. In this response, we affirm the importance of patient co-design, digital equity, data interoperability, and workforce education as essential enablers of VBHC. We also acknowledge the significance of sustainable financing models and stakeholder collaboration to support long-term transformation. We concur that realizing VBHC requires systemic alignment across governance, clinical, and financial dimensions, and we support continued interdisciplinary research to guide equitable and scalable digital health innovation.

not applicable