Enhancing Health Data Interoperability with Large Language Models: A
  FHIR Study
Yikuan Li Hanyin Wang Halid Yerebakan Yoshihisa Shinagawa Yuan Luo
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.