.png){kind=icon}
.png){kind=icon}
Return
Rethinking healthcare data interoperability in the age of large language models
.png){kind=icon}
OA
AI
DOI：10.1016/j.medj.2026.101146.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Electronic health records contain extensive real-world clinical data, but their effective use is hindered by data heterogeneity and interoperability challenges. Traditional post hoc standardization is costly and labor-intensive and reduces data granularity. Large language models enable analysis of unstructured data without full harmonization but lack precision for some tasks. We propose a hybrid strategy that combines large-language-model-based analysis of legacy data with prospectively standardized data, offering a scalable alternative that challenges the need for retrospective data harmonization and improves interoperability.
Keywords:
electronic health records
large language models
clinical data interoperability
data harmonization
ETL
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.8
Papers:
779
Citations:
2.2K