We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff, including physicians, nurses, insurance review and health records teams, and more. To construct the QA dataset on structured EHR data, we conducted a poll at a university hospital and templatized the responses to create seed questions. Then, we manually linked them to two open-source EHR databases, MIMIC-III and eICU, and included them with various time expressions and held-out unanswerable questions in the dataset, which were all collected from the poll. Our dataset poses a unique set of challenges: the model needs to 1) generate SQL queries that reflect a wide range of needs in the hospital, including simple retrieval and complex operations such as calculating survival rate, 2) understand various time expressions to answer time-sensitive questions in healthcare, and 3) distinguish whether a given question is answerable or unanswerable based on the prediction confidence. We believe our dataset, EHRSQL, could serve as a practical benchmark to develop and assess QA models on structured EHR data and take one step further towards bridging the gap between text-to-SQL research and its real-life deployment in healthcare. EHRSQL is available at https://github.com/glee4810/EHRSQL.

翻译：我们提出一个新的面向电子健康记录（EHR）的文本到SQL数据集。话语数据来自222名医院工作人员，包括医生、护士、医保审核和健康档案团队等。为构建结构化EHR数据上的问答数据集，我们在大学附属医院开展了一项调查，将受访者的回答模板化为种子问题。随后，我们将这些问题手动关联到两个开源EHR数据库（MIMIC-III和eICU），并纳入包含多种时间表达式的查询以及调查中收集的不可回答的留出问题。该数据集提出了一系列独特挑战：模型需要1）生成反映医院广泛需求的SQL查询，包括简单检索和计算存活率等复杂操作，2）理解各种时间表达式以解答医疗领域的时间敏感问题，3）基于预测置信度区分给定问题是否可回答。我们相信，该数据集（EHRSQL）可作为开发和评估结构化EHR数据上问答模型的实用基准，并将进一步弥合文本到SQL研究与其在实际医疗场景部署之间的差距。EHRSQL可访问 https://github.com/glee4810/EHRSQL。