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This study aims to tackle the critical challenge of adapting deep learning (DL) models for deployment in real-world healthcare settings, specifically focusing on catastrophic forgetting due to distribution shifts between hospital and non-hospital environments. Metabolic syndrome (MetS) is susceptible to misdiagnosis by DL models due to distribution shifts. This work demonstrates the potential of continual learning (CL) to enhance model performance in MetS identification across diverse settings.

To synthesize knowledge on tensions characterizing large-scale electronic health record (EHR) implementations.

Extracting social determinants of health (SDoHs) from medical notes depends heavily on labor-intensive annotations, which are typically task-specific, hampering reusability and limiting sharing. Here, we introduce SDoH-GPT, a novel framework leveraging few-shot learning large language models (LLMs) to automate the extraction of SDoH from unstructured text, aiming to improve both efficiency and generalizability.

Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed without considering the perspectives of the affected populations. This study describes the process of incorporating end-user perspectives into the development and implementation planning of a prediction algorithm for new perinatal depression onset.

Screening Brief Intervention and Referral to Treatment (SBIRT) can reduce the health and social costs associated with unhealthy alcohol use (UAU). Electronic health records (EHRs) can support evidence-based screening practices for UAU and provide performance data needed for quality improvement. The objective of this study was to describe barriers faced by primary care clinics when using EHR systems to support UAU screening and delivery of recommended interventions.

To determine how much of the current biomedical literature would be flagged or require modification in relation to the presence of terms from leaked lists prepared by the Centers for Disease Control (CDC), the National Science Foundation (NSF), and the National Security Administration (NSA) in early 2025.

The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development.

To examine the effects of message framing and visualization framing on the comprehension of personal health records and subsequent self-care behavioral intention among young and middle-older aged adults.

To conduct a scoping review (ScR) of existing approaches for synthetic Electronic Health Records (EHR) data generation, to benchmark major methods, and to provide an open-source software and offer recommendations for practitioners.

Unlocking clinical information embedded in clinical notes has been hindered to a significant degree by domain-specific and context-sensitive language. Identification of note sections and structural document elements has been shown to improve information extraction and dependent downstream clinical natural language processing (NLP) tasks and applications. This study investigates the viability of a dynamic example selection prompting method to section classification using lightweight, open-source large language models (LLMs) as a practical [...]