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

TL;DR

This paper explores using large language models to improve healthcare data interoperability by converting clinical texts into FHIR resources, achieving over 90% accuracy and streamlining processes.

Contribution

It introduces a novel application of LLMs for translating clinical texts into FHIR resources, demonstrating high accuracy and efficiency improvements.

Findings

Over 90% exact match accuracy with human annotations

Streamlined natural language processing workflow

Effective conversion of clinical texts to FHIR resources

Abstract

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.