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To conduct a scoping review of bias assessment in studies applying large language models (LLMs) to health data and to synthesize their prevailing conceptualization of bias.

Evaluate whether large language models reason or simply regurgitate training data in clinical diagnosis.

Despite the significant potential for Clinical Decision Support Systems (CDSSs) to improve care processes and health outcomes, several barriers hinder their widespread implementation in healthcare. While numerous systematic reviews have summarized potential barriers and facilitators for CDSS implementation, a comprehensive framework to guide and evaluate the implementation of CDSSs in healthcare is lacking. This overview of reviews, aims to establish a framework-GUIDE-CDSS-aimed at guiding and evaluating implementation of CDSSs in [...]

To understand how hospitals receive and use data on patients' health-related social needs (HRSN) from external sources, and how methods of receipt relate to use.

To examine the evolving role and application of SNOMED CT (SCT) during 2020-2025, a period marked by the COVID-19 pandemic and accelerated adoption of artificial intelligence (AI) in healthcare.

Computable phenotypes derived from electronic health records (EHRs) are central to clinical research and quality reporting. Although large language models (LLMs) can extract clinically rich information from unstructured notes, routine application to all patients is computationally expensive. We evaluated whether uncertainty-guided selective use of LLMs can improve phenotyping accuracy while preserving scalability.

Patients with rare diseases often face long delays before receiving a diagnosis. Using electronic health records for automated phenotyping and diagnosis of rare diseases is a promising approach but can be challenging because critical information is often recorded in unstructured notes rather than structured fields. This systematic review synthesizes the current literature applying natural language processing (NLP) and large language models (LLMs) for rare disease phenotyping and diagnosis from clinical [...]

To examine real-world barriers to implementing federated learning in healthcare and highlight the organizational, regulatory, and socio-technical factors often overlooked in technical research.

We report on findings from a meeting convened by the American College of Medical Informatics (ACMI) to characterize aspects of the patient experience that could be improved using informatics.

Home healthcare (HHC) clinical notes contain critical infection indicators that clinicians need in structured "indicator + context" pairs. Data sparsity and limited computing resources hinder automated extraction in decentralized HHC settings. This study developed and evaluated a resource-efficient pipeline using instruction-tuned, moderate-sized large language models (LLMs) to address these barriers. To address the data sparsity challenge, we also assessed the impact of a targeted LLM-based data augmentation strategy.