mlsanityData to 14 Oct 2026

Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records

arXiv 2405.06673 ↗First posted 4 May 2024cs.CL cs.AIcited by 11

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Abstract (arXiv)

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 were formed and completed the entire shared task requirement 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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 4 May 2024.

Builds on

13 of 32 references.

References · 13

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  3. 2024Self-Rewarding Language Modelscited by 681
  4. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  5. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  6. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  7. 2022Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectlycited by 89
  8. 2019Text-to-SQL Generation for Question Answering on Electronic Medical Recordscited by 77
  9. 2023How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settingscited by 58
  10. 2023Adaptation with Self-Evaluation to Improve Selective Prediction in LLMscited by 50
  11. 2024TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoringcited by 17
  12. 2024ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handlingcited by 7
1 more
  1. 2024Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQLcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 5.

11 papers.

  1. 2024MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applicationscited by 25
  2. 2025FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answeringcited by 22
  3. 2025M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysiscited by 3
  4. 2026EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Recordscited by 1
  5. 2025Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraintscited by 0
  6. 2025SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answeringcited by 0
  7. 2025Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQLcited by 0
  8. 2026CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domaincited by 0
  9. 2026FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM datacited by 0
  10. 2026Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agentscited by 0
  11. 2026ProCARE: Real-World Study Automation via Profile-Grounded Evidence Contractscited by 0