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            <namePart type="given">Deepak</namePart>
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    <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 1overall by +2.0%. Our final submission resulted in 77.4% by F 1overall, which positions us at 10th out of 16 teams. Our code is publicly available: https://github.com/nicolay-r/ArchEHR-QA-2026-Task-4-MedEvi-NS</abstract>
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%0 Conference Proceedings
%T MedEvi-NS at ArchEHR-QA 2026: Using Clinical Reasoning Principles to Improve Zero-shot Capabilities of Large Language Models in Evidence Alignment
%A Sun, Mengxuan
%A Rusnachenko, Nicolay
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F sun-rusnachenko-2026-medevi
%X 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 1overall by +2.0%. Our final submission resulted in 77.4% by F 1overall, which positions us at 10th out of 16 teams. Our code is publicly available: https://github.com/nicolay-r/ArchEHR-QA-2026-Task-4-MedEvi-NS
%R 10.63317/2uoe94adhkng
%U https://aclanthology.org/2026.cl4health-1.44/
%U https://doi.org/10.63317/2uoe94adhkng
%P 482-486

Markdown (Informal)

[MedEvi-NS at ArchEHR-QA 2026: Using Clinical Reasoning Principles to Improve Zero-shot Capabilities of Large Language Models in Evidence Alignment](https://aclanthology.org/2026.cl4health-1.44/) (Sun & Rusnachenko, CL4Health 2026)

• MedEvi-NS at ArchEHR-QA 2026: Using Clinical Reasoning Principles to Improve Zero-shot Capabilities of Large Language Models in Evidence Alignment (Sun & Rusnachenko, CL4Health 2026)

ACL

• Mengxuan Sun and Nicolay Rusnachenko. 2026. MedEvi-NS at ArchEHR-QA 2026: Using Clinical Reasoning Principles to Improve Zero-shot Capabilities of Large Language Models in Evidence Alignment. In Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026, pages 482–486, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).