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Enhancing Health Data Interoperability with Large Language Models: A FHIR Study

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arxiv 2310.12989 v1 pith:YDKIVPHS submitted 2023-09-19 cs.CL cs.AI

Yikuan Li , Hanyin Wang , Halid Yerebakan , Yoshihisa Shinagawa , Yuan Luo This is my paper · ORCID
classification cs.CLcs.AI
keywords languageclinicaldatafhirhumaninteroperabilitylargeability
verification ladder T0 review T1 audit T2 compute T3 formal
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In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their corresponding FHIR resources. Our experiments, conducted on 3,671 snippets of clinical text, demonstrated that the LLM not only streamlines the multi-step natural language processing and human calibration processes but also achieves an exceptional accuracy rate of over 90% in exact matches when compared to human annotations.

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  1. Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare

    cs.LG 2025-05 conditional novelty 4.0 of 10

    An ontology-retrieval plus LLM-adjudication pipeline maps EHR outcomes to MONDO/HPO codes with 78% to 92% agreement against a human expert reviewer.

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