Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 22, 2024
Date Accepted: Apr 27, 2024
Redefining Health Care Data Interoperability: An Empirical Exploration of Large Language Models in Information Exchange
ABSTRACT
Background:
Efficient data exchange and health care interoperability are impeded by medical records often being in non-standardized 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 evaluates the capability of LLMs in transforming and transferring health care data to support interoperability.
Methods:
Utilizing data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted three experiments. Experiment 1 assessed the accuracy of transforming structured lab results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the International Classification of Diseases, Ninth Revision, Clinical Modification and SNOMED Clinical Terms 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 lab 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. Clinical Trial: N/A
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