Search AMIA.org

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.

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 [...]

This study aims to review the effectiveness of electronic patient-reported outcome measures (ePROMs) to triage and schedule appointments for adult patients with chronic medical conditions.

Health-care institutions customize electronic health record (EHR) configurations to reflect their unique workflows and patient care priorities. Ensuring EHR alignment across sites facilitates seamless information exchange. We developed a pipeline for EHR flowsheet alignment between health-care organizations. The pipeline is augmented by mapping flowsheet data fields to concepts in the Clinical Care Classification (CCC) nursing terminology.

Establishing a robust and secure framework allowing creation and sharing of custom features within the REDCap electronic data capture platform.

Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task.