@inproceedings{balachandran-etal-2025-exl,
title = "{EXL} Health {AI} Lab at {MEDIQA}-{OE} 2025: Evaluating Prompting Strategies with {M}ed{G}emma for Medical Order Extraction",
author = "Balachandran, Abhinand and
Durgapraveen, Bavana and
Sudhagar, Gowsikkan Sikkan and
S, Vidhya Varshany J and
Rajkumar, Sriram",
editor = "Ben Abacha, Asma and
Bethard, Steven and
Bitterman, Danielle and
Naumann, Tristan and
Roberts, Kirk",
booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
month = oct,
year = "2025",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clinicalnlp-1.8/",
pages = "68--74",
abstract = "The accurate extraction of medical orders fromdoctor-patient conversations is a critical taskfor reducing clinical documentation burdensand ensuring patient safety. This paper detailsour team{'}s submission to the MEDIQA-OE-2025Shared Task. We investigate the performanceof MedGemma, a new domain-specific opensource language model, for structured order extraction. We systematically evaluate three distinct prompting paradigms: a straightforwardone-shot approach, a reasoning-focused ReActframework, and a multi-step agentic workflow.Our experiments reveal that while more complex frameworks like ReAct and agentic flowsare powerful, the simpler one-shot promptingmethod achieved the highest performance onthe official validation set. We posit that on manually annotated transcripts, complex reasoningchains can lead to ``overthinking'' and introduce noise, making a direct approach more robust and efficient. Our work provides valuableinsights into selecting appropriate promptingstrategies for clinical information extraction invaried data conditions."
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<abstract>The accurate extraction of medical orders fromdoctor-patient conversations is a critical taskfor reducing clinical documentation burdensand ensuring patient safety. This paper detailsour team’s submission to the MEDIQA-OE-2025Shared Task. We investigate the performanceof MedGemma, a new domain-specific opensource language model, for structured order extraction. We systematically evaluate three distinct prompting paradigms: a straightforwardone-shot approach, a reasoning-focused ReActframework, and a multi-step agentic workflow.Our experiments reveal that while more complex frameworks like ReAct and agentic flowsare powerful, the simpler one-shot promptingmethod achieved the highest performance onthe official validation set. We posit that on manually annotated transcripts, complex reasoningchains can lead to “overthinking” and introduce noise, making a direct approach more robust and efficient. Our work provides valuableinsights into selecting appropriate promptingstrategies for clinical information extraction invaried data conditions.</abstract>
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%0 Conference Proceedings
%T EXL Health AI Lab at MEDIQA-OE 2025: Evaluating Prompting Strategies with MedGemma for Medical Order Extraction
%A Balachandran, Abhinand
%A Durgapraveen, Bavana
%A Sudhagar, Gowsikkan Sikkan
%A S, Vidhya Varshany J.
%A Rajkumar, Sriram
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 7th Clinical Natural Language Processing Workshop
%D 2025
%8 October
%I Association for Computational Linguistics
%C Virtual
%F balachandran-etal-2025-exl
%X The accurate extraction of medical orders fromdoctor-patient conversations is a critical taskfor reducing clinical documentation burdensand ensuring patient safety. This paper detailsour team’s submission to the MEDIQA-OE-2025Shared Task. We investigate the performanceof MedGemma, a new domain-specific opensource language model, for structured order extraction. We systematically evaluate three distinct prompting paradigms: a straightforwardone-shot approach, a reasoning-focused ReActframework, and a multi-step agentic workflow.Our experiments reveal that while more complex frameworks like ReAct and agentic flowsare powerful, the simpler one-shot promptingmethod achieved the highest performance onthe official validation set. We posit that on manually annotated transcripts, complex reasoningchains can lead to “overthinking” and introduce noise, making a direct approach more robust and efficient. Our work provides valuableinsights into selecting appropriate promptingstrategies for clinical information extraction invaried data conditions.
%U https://aclanthology.org/2025.clinicalnlp-1.8/
%P 68-74
Markdown (Informal)
[EXL Health AI Lab at MEDIQA-OE 2025: Evaluating Prompting Strategies with MedGemma for Medical Order Extraction](https://aclanthology.org/2025.clinicalnlp-1.8/) (Balachandran et al., ClinicalNLP 2025)
ACL