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FHIR-GPT Enhances Health Interoperability with Large Language Models.

Yikuan Li1,2, Hanyin Wang1, Halid Z Yerebakan2

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Large language models (LLMs) can effectively convert unstructured clinical text into Fast Healthcare Interoperability Resources (FHIR) medication statements. This advancement significantly enhances health data interoperability for research and public health initiatives.

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Artificial Intelligence in Healthcare

Background:

  • Health data interoperability is crucial for research, clinical trials, and public health surveillance.
  • The Fast Healthcare Interoperability Resources (FHIR) standard is promoted by federal agencies to advance interoperability.
  • Transforming heterogeneous electronic health record data, especially unstructured text, into FHIR resources presents significant challenges.

Purpose of the Study:

  • To assess the capability of large language models (LLMs) in converting clinical narratives into FHIR resources.
  • To develop and evaluate a specific LLM, FHIR-GPT, for transforming clinical texts into FHIR medication statements.

Main Methods:

  • Development of FHIR-GPT, a generative pretrained transformer model tailored for clinical text to FHIR medication statement transformation.
  • Experimental evaluation using 3671 snippets of clinical texts to compare FHIR-GPT performance against existing methods.

Main Results:

  • FHIR-GPT achieved an exact match rate exceeding 90% in transforming clinical texts into FHIR medication statements.
  • FHIR-GPT demonstrated substantial improvements over existing NLP pipelines, particularly in extracting timing schedules (over 50%), forms (42%), and reasons (35%).

Conclusions:

  • Large language models show significant potential for enhancing health data interoperability by efficiently converting unstructured clinical narratives into structured FHIR resources.
  • FHIR-GPT offers a promising solution to overcome the challenges associated with health data transformation, reducing development costs and improving accuracy.