EHR QA

EHR: Electronic Health Record · QA: Question Answering

Latest papers

18 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Conversational Interaction for LLM-Based Clinical Text-to-SQL: A User Study with Healthcare Professionals
Oct 31, 2026Jun-Min Lee, Suyoung Yoo, Gyubok Lee +2Text-to-SQL Human-Computer Interaction
Department of Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Seongnam-si, 13558, Gyeonggi-do, Republic of Korea. · Research Institute for Future Medicine, Samsung Medical Center, Seoul, 06351, Republic of Korea. · Department of Emergency Medicine, Samsung Medical Center, Seoul, 06351, Republic of Korea. +1
Can JEV Support Clinical Trial Criteria Assessment?
Oct 26, 2026Mengxian He, Ziyue Gao, Chun Li +4Clinical Decision Support Clinical Language Model Evaluation
The Chinese University of Hong Kong · Capital Medical University
DocTalk: Enhancing Medical Knowledge Retrieval via Fine-tuned Large Language Models with Chain-of-Thought Reasoning for Robust Clinical Decision Support
Oct 26, 2026Asad Gohar, Md Ariful Islam Mozumder, Moon-Il Joo +1Clinical QA EHR QA
Institute of Digital Anti-Aging HealthCare, Inje University, Gimhae 50834, Republic of Korea
A general-purpose System One decision model for clinical records
Oct 19, 2026Andrew Zhang, Trinh T. L. Vuong, Sophia J. Wagner +14EHR QA EHR Foundation Models
Mass General Brigham AI Institute, Boston, MA. · Harvard Medical School, Boston, MA. · Massachusetts Institute of Technology, Cambridge, MA.
Evaluating Biomedical Reranking for LLM-Based Question Answering over Longitudinal Clinical Notes
Oct 1, 2026Maryam Shahbaz Ali, Laura B. Strachan, Caitlin Sherman +3Cross-Encoder Reranking Retrieval-Augmented Generation
Children’s National Hospital, Washington, DC, USA · University of Florida, Gainesville, FL, USA · George Washington University, Washington DC, USA
EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning
Jun 22, 2026Yitong Qiao, Lei Liu, Yue Shen +4AI Agent Benchmarks Clinical Reasoning
1Zhejiang University · †Work done during an internship at Ant Group. · 2Ant Group
Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering
Jun 15, 2026Sanjay BasuMulti-Hop QA Compositional Reasoning
Department of Medicine, University of California San Francisco, San Francisco, CA · Waymark, San Francisco, CA
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
Jun 14, 2026Jiyoun Kim, Muhan Yeo, Eunhye Jang +14Clinical QA Evidence-Grounded Generation
KAIST · Seoul National University · Seoul National University Bundang Hospital +9
Evaluating the Utility of Personal Health Records in Personalized Health AI
May 18, 2026Rory Sayres, Kejia Chen, Ayush Jain +19LLM Personalization Healthcare
1Google Research · †Work done while at Google Research
ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV
May 11, 2026Alex StinardRAG Evaluation Clinical QA
Department of Clinical Sciences, College of Medicine · University of Central Florida, Orlando, FL 32816
Neural at ArchEHR-QA 2026: One Method Fits All: Unified Prompt Optimization for Clinical QA over EHRs
May 11, 2026Abrar Majeedi, Viswanatha Reddy Gajjala, Sai Prasanna Teja Reddy Bogireddy +1Healthcare Clinical QA
University of Wisconsin–Madison · University of Chicago · Independent Researcher
HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering
Apr 29, 2026Md Biplob Hosen, Md Alomgeer Hussein, Md Akmol Masud +3LLM Grounding Healthcare
University of Maryland Baltimore County, Baltimore, MD, USA · 2Jahangirnagar University, Savar, Dhaka, Bangladesh
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
Apr 22, 2026Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu +1Healthcare Hierarchical Representation Learning
Department of Computer Science, Stony Brook University · Department of Data Science, New Jersey Institute of Technology · Department of Biomedical Informatics, Stony Brook University
Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering
Apr 8, 2026Elyas Irankhah, Samah FodehEHR QA Clinical QA
Yale University, Department of Emergency Medicine, New Haven, CT, USA
Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models
Mar 27, 2026Mikko Saukkoriipi, Nicole Hernandez, Jaakko Sahlsten +2EHR QA Clinical QA
Department of Computer Science, Aalto University School of Science, Espoo, 02150, Finland. · Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland. · Department of Oncology, TAYS Cancer Centrer, Tampere University Hospital, Tampere, 33520, Finland.
QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment
Mar 26, 2026Mohammad AL-SmadiClinical QA EHR QA
Qatar University Doha, Qatar
sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook
Mar 14, 2026Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa +1EHR QA Clinical QA
Technical University of Munich, School of Computation, Information and Technology, Germany
FHIRPath-QA: Executable Question Answering over FHIR Electronic Health Records
Feb 26, 2026Michael Frew, Nishit Bheda, Bryan TrippEHR QA Clinical QA
University of British Columbia · University of Waterloo

Oct 31, 2026J/K move · Enter open · S save

Conversational Interaction for LLM-Based Clinical Text-to-SQL: A User Study with Healthcare Professionals

Jun-Min Lee, Suyoung Yoo, Gyubok Lee, Edward Choi+1

Department of Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology, Seongnam-si, 13558, Gyeonggi-do, Republic of Korea. · Research Institute for Future Medicine, Samsung Medical Center, Seoul, 06351, Republic of Korea. · Department of Emergency Medicine, Samsung Medical Center, Seoul, 06351, Republic of Korea.+1

Text-to-SQL Human-Computer Interaction EHR QA Multi-Turn Dialogue Evaluation

Open paper AskPDF arXiv

Conversational text-to-SQL interfaces could give healthcare professionals natural-language access to electronic health record (EHR) data, but evidence for their real-world usability remains limited: prior evaluations have relied largely on curated benchmarks with fully specified questions or on simulated users. We report a user study in which 14 physicians who regularly work with EHR data used both a single-turn and a conversational multi-turn text-to-SQL interface over the MIMIC-IV database in a within-subjects design; dialogue analyses additionally drew on interaction logs from 21 healthcare professionals across the main study and two pilot studies. The conversational interface was consistently favored: mean System Usability Scale scores rose from 68.2, near the industry average, to 85.9 (p<0.001), with significant advantages across technology-acceptance constructs including performance expectancy, effort expectancy, self-efficacy, and behavioral intention. In post-study forced-choice comparisons, 93% of participants reported greater overall satisfaction with the conversational interface, 86% preferred it for real-world adoption, and 71% trusted its results more; no participant preferred or trusted the single-turn interface more. Turn-level analysis of 425 logged user turns showed that freely composed queries triggered clarification requests in 24.8% of cases versus 1.8% for predefined, fully specified questions, and 64.3% of clarification episodes stemmed from EHR- or clinical-domain knowledge. Conversational interaction is a practical prerequisite, rather than a convenience, for natural-language EHR access. The frequent need for clarification in realistic clinical queries further underscores the importance of conversational interfaces, while benchmark-style evaluation can substantially underrepresent the ambiguity of real clinical use.