LAMAR at ArchEHR-QA 2025: Clinically Aligned LLM-Generated Few-Shot Learning for EHR-Grounded Patient Question Answering
Seksan Yoadsanit, Nopporn Lekuthai, Watcharitpol Sermsrisuwan, Titipat Achakulvisut · 2025
This paper presents an approach to answering patient-specific medical questions using electronic health record (EHR) grounding with ArchEHR-QA 2025 datasets.We address medical question answering as an alignment problem, focusing on generating responses factually consistent with patient-specific clinical notes through in-context learning techniques.We show that LLM-generated responses, used as few-shot examples with GPT-4.1 and Gemini-2.5-Pro,significantly outperform baseline approaches (overall score = 49.1),achieving strict precision, recall, and F1-micro scores of 60.6, 53.6, and 56.9, respectively, on the ArchEHR-QA 2025 test leaderboard.It achieves textual similarity between answers and essential evidence using BLEU, ROUGE, SARI, BERTScore, Align-Score, and MEDCON scores of 6.0, 32.1, 65.8, 36.4,64.3, and 43.6, respectively.Our findings highlight the effectiveness of combining EHR grounding with few-shot examples for personalized medical question answering, establishing a promising approach for developing accurate and personalized medical question answering systems.We release our code at https://github.com/biodatlab/archehr-qa-lamar.