Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model.

ABSTRACT:

Importance

Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across structured and unstructured data, and due to low interoperability across systems. Traditionally, extracting this information requires manual EHR abstraction, a time-consuming and expensive process that also limits real-time care quality improvement.

Objective

To evaluate whether a data format-agnostic retrieval-augmented generation-enabled large language model (RAG-LLM) can accurately abstract clinical variables from heterogeneous structured and unstructured EHR data.

Design setting and participants

Retrospective cross-sectional study assessing stroke and bleeding risk in patients with atrial fibrillation (AF) fr

SUBMITTER: Adejumo P 

PROVIDER: S-EPMC11451809 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature