MedEvi-NS at ArchEHR-QA 2026: Using Clinical Reasoning Principles to Improve Zero-shot Capabilities of Large Language Models in Evidence Alignment
Year: 2026
Type: conference-paper
Abstract: The ArchEHR-QA shared task focuses on grounded question answering using patient EHR data.For the given clinical interpretation of the patient question, note excerpt (E) and answer text (A), subtask 4 (evidence alignment) aims to cite supporting sentences from E for each sentence in A. In this paper, we propose a prompt-engineering methodology that features clinical-reasoning principles in related alignment.We adopt this methodology for GPT-5.2 in zero-shot learning mode.According to our experiments on ArchEHR-QA, incorporating clinical reasoning principles into the prompt improves F 1 overall by +2.0%.Our final submission resulted in 77.4% by F 1 overall , which positions us at 10 th out of 16 teams.Our code is publicly available: https://github.com/nicolay-r/ArchEHR-QA-2026-
Authors: Mengxuan Sun, Nicolay Rusnachenko
Institutions: University of Aberdeen, Bournemouth University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7166528008