Electronic Health Records (EHRs) are relational databases that store the
entire medical histories of patients within hospitals. They record numerous
aspects of patients' medical care, from hospital admission and diagnosis to
treatment and discharge. While EHRs are vital sources of clinical data,
exploring them beyond a predefined set of queries requires skills in query
languages like SQL. To make information retrieval more accessible, one strategy
is to build a question-answering system, possibly leveraging text-to-SQL models
that can automatically translate natural language questions into corresponding
SQL queries and use these queries to retrieve the answers. The EHRSQL 2024
shared task aims to advance and promote research in developing a
question-answering system for EHRs using text-to-SQL modeling, capable of
reliably providing requested answers to various healthcare professionals to
improve their clinical work processes and satisfy their needs. Among more than
100 participants who applied to the shared task, eight teams completed the
entire shared task processes and demonstrated a wide range of methods to
effectively solve this task. In this paper, we describe the task of reliable
text-to-SQL modeling, the dataset, and the methods and results of the
participants. We hope this shared task will spur further research and insights
into developing reliable question-answering systems for EHRs.

电子健康档案（EHRs）是存储医院患者整个病史的关系型数据库，通过构建问答系统、利用文本到 SQL 的模型将自然语言问题转化为对应的 SQL 查询并使用这些查询检索答案，可以提供医疗专业人员所需的答案，改进其临床工作流程并满足其需求。