Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraints
This addresses the barrier for medical researchers lacking technical skills to efficiently use EHR data, though it is incremental as it builds on existing LLM and SQL generation methods.
The paper tackles the problem of enabling medical researchers to query electronic health records (EHRs) without database expertise by introducing CELEC, an LLM-powered framework that translates natural language to SQL, achieving execution accuracy comparable to prior systems on the EHRSQL benchmark while maintaining low latency, cost efficiency, and strict privacy.
Electronic health records (EHRs) are central to modern healthcare delivery and research; yet, many researchers lack the database expertise necessary to write complex SQL queries or generate effective visualizations, limiting efficient data use and scientific discovery. To address this barrier, we introduce CELEC, a large language model (LLM)-powered framework for automated EHR data extraction and analytics. CELEC translates natural language queries into SQL using a prompting strategy that integrates schema information, few-shot demonstrations, and chain-of-thought reasoning, which together improve accuracy and robustness. On a subset of the EHRSQL benchmark, CELEC achieves execution accuracy comparable to prior systems while maintaining low latency, cost efficiency, and strict privacy by exposing only database metadata to the LLM. CELEC also adheres to strict privacy protocols: the LLM accesses only database metadata (e.g., table and column names), while all query execution occurs securely within the institutional environment, ensuring that no patient-level data is ever transmitted to or shared with the LLM. Ablation studies confirm that each component of the SQL generation pipeline, particularly the few-shot demonstrations, plays a critical role in performance. By lowering technical barriers and enabling medical researchers to query EHR databases directly, CELEC streamlines research workflows and accelerates biomedical discovery.