Article

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

2024-09-22

Abstract excerpt

<h4>Importance</h4> 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....

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bb20e262-39ea-5bbd-8c62-fecc17bec7ed
DOI: 10.1101/2024.09.19.24313992

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language ModelDOI 10.1101/2024.09.19.24313992Read

Large Language Models in Cardiovascular Prevention: From Potential to PracticeSemantic proximity Read

General-purpose large language models can achieve physician-level accuracy in complex medical data extractionSemantic proximity Read

Bridging AI and Healthcare: A Scoping Review of Retrieval-Augmented Generation—Ethics, Bias, Transparency, Improvements, and ApplicationsSemantic proximity Read

Targeted use of large language models for EHR-based computable phenotyping.Semantic proximity Read

Assessing Retrieval-Augmented Large Language Model Performance in Emergency Department ICD-10-CM Coding Compared to Human CodersSemantic proximity Read

Scalable information extraction from free text electronic health records using large language modelsSemantic proximity Read

Evaluating large language model workflows in clinical decision support: referral, triage, and diagnosisSemantic proximity Read

Assessing the Limitations of Large Language Models in Clinical Practice Guideline-concordant Treatment Decision-making on Real-world DataSemantic proximity Read

Artificial Intelligence for Automated, Highly Accurate, and Scalable Multimodal EHR Data AbstractionSemantic proximity Read

Verifiable Summarization of Electronic Health Records Using Large Language Models to Support Chart ReviewSemantic proximity Read

Scalable information extraction from free text electronic health records using large language modelsSemantic proximity Read

FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision SupportSemantic proximity Read