Search AMIA.org

Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.

To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.

Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone. This paper presents an ablation study exploring how different embedding models and pooling methods affect information retrieval for the clinical domain.

To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

Event capture in clinical trials is resource-intensive, and electronic medical records (EMRs) offer a potential solution. This study develops algorithms for EMR-based death and hospitalization capture and compares them with traditional event capture methods.

Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.

To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.