ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality
By: Adrián Cuadrón , Aimar Sagasti , Maitane Urruela and more
Potential Business Impact:
Helps doctors answer patient questions from notes.
This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.
Similar Papers
Neural at ArchEHR-QA 2025: Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering
Machine Learning (CS)
Helps doctors find patient info faster.
UTSA-NLP at ArchEHR-QA 2025: Improving EHR Question Answering via Self-Consistency Prompting
Computation and Language
Answers doctor questions using patient records.
A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization
Computation and Language
Helps doctors answer patient questions using health records.