ArticleJournal of medical Internet research2024
Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions.Nutrients · 2026Pooled it
- Bridging primary care with artificial intelligence: toward a global 1.5-Tier Healthcare System.npj health systems · 2026Review
- Automated Identification of Nursing Diagnoses and Interventions From Nursing Records Using a Retrieval-Augmented Large Language Model Approach: Quantitative Study.Journal of medical Internet research · 2026Article
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- An Evaluation Framework for Large Language Models in Clinical Nursing: A Scoping Review and Expert Consultation.Journal of nursing management · 2026Article
- Artificial intelligence for autoimmune diseases.NPJ digital medicine · 2025Article
- Extracting Pulmonary Embolism Diagnoses From Radiology Impressions Using GPT-4o: Large Language Model Evaluation Study.JMIR medical informatics · 2025Article
- Standardizing free-text data exemplified by two fields from the Immune Epitope Database.Journal of biomedical semantics · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEfficient 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.
objectiveThis study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability.
methodsUsing 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).
resultsThe 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.
conclusionsThis 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.
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