%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e56614
%T Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange
%A Yoon,Dukyong
%A Han,Changho
%A Kim,Dong Won
%A Kim,Songsoo
%A Bae,SungA
%A Ryu,Jee An
%A Choi,Yujin
%+ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro Seodaemun-gu, Seoul, 03722, Republic of Korea, 82 31 5189 8450, dukyong.yoon@yonsei.ac.kr
%K health care interoperability
%K large language models
%K medical data transformation
%K data standardization
%K text-based
%D 2024
%7 31.5.2024
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. Objective: This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. Methods: Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). Results: The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. Conclusions: This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure. 
%M 38819879
%R 10.2196/56614
%U https://www.jmir.org/2024/1/e56614
%U https://doi.org/10.2196/56614
%U http://www.ncbi.nlm.nih.gov/pubmed/38819879