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Data, information, and knowledge in health care has expanded exponentially over the past 50 years, leading to significant challenges with information overload and complex, fragmented care plans. Generative artificial intelligence (AI) has the potential to facilitate summarization and integration of knowledge to enable efficient care planning and reduce cognitive load of clinicians.

Gender disparities in academic medicine have been previously reported, but prior analyses have relied on either manual labor or fixed databases of name-gender pairs that fail to generalize across different populations and cultures. The objective of this work is to evaluate the utility of large language models (LLMs) as a potential tool to facilitate systematic bibliometric analysis of academic research trends.

This integrative review aims to analyze the REDCap-based patient-facing technologies (PFTs) and propose a tailored maturity model to characterize their development stages, challenges, and future evolution toward intelligent, proactive care.

Prospective studies examining electronic health record (EHR) implementation in multi-professional healthcare provider samples remain scarce, particularly with respect to impacts on providers' workflows and outcomes in real-world clinical settings.

The Hospital Authority in Hong Kong developed an application, known as 'HA Go', to enhance patients' healthcare journeys and optimize the efficiency of healthcare resource allocation. This study aimed to explore the perceptions of HA Go among the key stakeholders and identify barriers and facilitators to its adoption.

Despite the recognized need to leverage health information technology (IT) to facilitate error detection, there are few health IT safety measures in use and limited resources available to guide the conceptualization, development, and testing of new measures. Audit log data provide a rich source of clinician behaviors within the electronic health record (EHR), which can be used to develop health IT safety measures.

This methodological validation study evaluated a large language model (LLM), multi-step framework for thematic analysis (TA) of healthcare interviews, compared to a human-only coded reference standard.

Clinical narrative provides a unique window into provider reasoning and attribution for automated diagnosis assignment, but large language models (LLMs) have traditionally not performed well at medical coding. We evaluate a reproducible method for automated diagnosis assignment using LLMs in clinical notes and compare with structured diagnoses.

Clinical AI systems and models increasingly need traceable documentation that makes intended use, performance evidence, transparency, and lifecycle status explicit, yet existing model cards, datasheets, reporting guidelines, and other documentation approaches do not consistently bring together structured clinical fields, vocabulary-linked cohort descriptions, structured performance reporting, and governed documentation history. We introduce Structured, Meaningful, Auditable, Responsible, and Transparent (SMART), a framework that adapts the model card concept into structured, lifecycle-aware documentation [...]