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We present a new text-to-SQL dataset for electronic health records (EHRs).The utterances were collected from 222 hospital staff members, includingphysicians, nurses, and insurance review and health records teams. To constructthe QA dataset on structured EHR data, we conducted a poll at a universityhospital and used the responses to create seed questions. We then manuallylinked these questions to two open-source EHR databases, MIMIC-III and eICU,and included various time expressions and held-out unanswerable questions inthe dataset, which were also collected from the poll. Our dataset poses aunique set of challenges: the model needs to 1) generate SQL queries thatreflect a wide range of needs in the hospital, including simple retrieval andcomplex operations such as calculating survival rate, 2) understand varioustime expressions to answer time-sensitive questions in healthcare, and 3)distinguish whether a given question is answerable or unanswerable. We believeour dataset, EHRSQL, can serve as a practical benchmark for developing andassessing QA models on structured EHR data and take a step further towardsbridging the gap between text-to-SQL research and its real-life deployment inhealthcare. EHRSQL is available at https://github.com/glee4810/EHRSQL.

EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records