Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records
This addresses the problem of complex SQL query generation for epidemiological analysis in healthcare, though it appears incremental as it builds on existing text-to-SQL and RAG techniques.
The paper tackled the challenge of querying electronic health records and claims data for epidemiological questions by introducing an end-to-end methodology combining text-to-SQL generation with retrieval augmented generation (RAG), resulting in significant performance improvements over simple prompting.
Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. Querying these databases to answer epidemiological questions is challenging due to the intricacy of medical terminology and the need for complex SQL queries. Here, we introduce an end-to-end methodology that combines text-to-SQL generation with retrieval augmented generation (RAG) to answer epidemiological questions using EHR and claims data. We show that our approach, which integrates a medical coding step into the text-to-SQL process, significantly improves the performance over simple prompting. Our findings indicate that although current language models are not yet sufficiently accurate for unsupervised use, RAG offers a promising direction for improving their capabilities, as shown in a realistic industry setting.