Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.

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.

Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data.

Cardiovascular disease (CVD) remains a pressing global health concern. While traditional risk prediction methods such as the Framingham and American College of Cardiology/American Heart Association (ACC/AHA) risk scores have been widely used in the practice, artificial intelligence (AI), especially GPT-4, offers new opportunities. Utilizing large scale of multi-center data from 47,468 UK Biobank participants and 5,718 KoGES participants, this study quantitatively evaluated the predictive capabilities of GPT-4 in comparison with traditional models. Our results suggest that the GPT-based score showed commendably comparable performance in CVD prediction when compared to traditional models (AUROC on UKB: 0.725 for GPT-4, 0.733 for ACC/AHA, 0.728 for Framingham; KoGES: 0.664 for GPT-4, 0.674 for ACC/AHA, 0.675 for Framingham). Even with omission of certain variables, GPT-4's performance was robust, demonstrating its adaptability to data-scarce situations. In conclusion, this study emphasizes the promising role of GPT-4 in predicting CVD risks across varied ethnic datasets, pointing toward its expansive future applications in the medical practice.

Diagnostic value of pyrosequencing for the BRAF V600E mutation in ultrasound-guided fine-needle aspiration biopsy samples of thyroid incidentalomas.

CONTEXT: Dideoxy sequencing is the most commonly used method for detecting the BRAF(V600E) mutation in thyroid cancer and melanoma. However, this gold standard method often makes less definite results in detecting the BRAF(V600E) mutation when there are relatively low amounts of the mutant template in biopsy specimens, which are invariably contaminated with normal tissues. Pyrosequencing, which measures the incorporation of each of the four nucleotides at each template position and indicates the amounts of mutant template present, may be more useful in such situations. OBJECTIVE: To investigate the diagnostic efficiency of pyrosequencing for the mutant BRAF allele in ultrasound (US)-guided fine needle aspiration biopsies (FNABs) of thyroid incidentalomas. DESIGN, SETTING AND SUBJECTS: A total of 101 thyroid incidentaloma cases were included prospectively. Cytological diagnoses of the FNAB samples were made according to the American Thyroid Association (ATA) guidelines, 2006. The presence of the BRAF(V600E) mutation was investigated by pyrosequencing and dideoxy sequencing. RESULTS: On the basis of cytological analysis, the thyroid incidentalomas were classified into benign (n = 43), malignant (n = 30), indeterminate or suspicious neoplasm (n = 24), and nondiagnostic (n = 4) categories. Pyrosequencing detected the BRAF(V600E) mutation in 30 cases: 22 malignant cases, 7 indeterminate cases, and 1 nondiagnostic case. Dideoxy sequencing also detected the BRAF(V600E) mutation in 28 of the same cases but failed to clearly distinguish the mutant allele from the wild-type allele in one indeterminate case and one nondiagnostic case. Histopathological analysis ascertained that all BRAF(V600E)-positive cases were papillary thyroid carcinomas. CONCLUSIONS: Pyrosequencing may be suitable for detecting the BRAF(V600E) mutation in thyroid incidentaloma and may be superior to dideoxy sequencing when low amounts of the mutant template are present in the biopsy.