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

• URL: http://arxiv.org/abs/2310.12989v1
• Date: Tue, 19 Sep 2023 20:09:35 GMT
• Title: Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
• Authors: Yikuan Li, Hanyin Wang, Halid Yerebakan, Yoshihisa Shinagawa and Yuan Luo
• Abstract summary: We investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. 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.
• Score: 8.918194175207702
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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