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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 [...]

Accurate citation of relevant publications is essential for scientific integrity in biomedical research. Large language models (LLMs) excel at text generation but often hallucinate fabricated or inaccurate citations. Retrieval-augmented generation (RAG) can mitigate these errors, yet current approaches lack semantic precision in evidence retrieval. This study aims to develop a domain-specific RAG system for reliable, context-specific biomedical citation recommendations.

Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies.