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.

引入一种基于文本到 SQL 生成和检索增强生成（RAG）的端到端方法，利用电子健康记录和索赔数据回答流行病学问题，研究表明该方法显著提高了性能，对提高语言模型能力有应用前景。