Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models

Ariel Guerra-Adames, Marta Avalos-Fernandez, Océane Doremus, Cédric Gil-Jardiné, Emmanuel Lagarde
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:420-439, 2025.

Abstract

Judgment biases in emergency triage can adversely affect patient outcomes. This study examines sex/gender biases using four advanced language models fine-tuned on real-world emergency department data. We introduce a novel approach based on the testing method, commonly used in hiring bias detection, by automatically altering triage notes to change patient sex references. Results indicate a significant bias: female patients are assigned lower severity ratings than male patients with identical clinical conditions. This bias is more pronounced with female nurses or when patients report higher pain levels but diminishes with increased nurse experience. Identifying these biases can inform interventions such as enhanced training, protocol updates, and machine learning tools to support clinical decision-making.

Cite this Paper

BibTeX
@InProceedings{pmlr-v259-guerra-adames25a, title = {Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models}, author = {Guerra-Adames, Ariel and Avalos-Fernandez, Marta and Doremus, Oc{\'{e}}ane and Gil-Jardin{\'{e}}, C{\'{e}}dric and Lagarde, Emmanuel}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {420--439}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/guerra-adames25a/guerra-adames25a.pdf}, url = {https://proceedings.mlr.press/v259/guerra-adames25a.html}, abstract = {Judgment biases in emergency triage can adversely affect patient outcomes. This study examines sex/gender biases using four advanced language models fine-tuned on real-world emergency department data. We introduce a novel approach based on the testing method, commonly used in hiring bias detection, by automatically altering triage notes to change patient sex references. Results indicate a significant bias: female patients are assigned lower severity ratings than male patients with identical clinical conditions. This bias is more pronounced with female nurses or when patients report higher pain levels but diminishes with increased nurse experience. Identifying these biases can inform interventions such as enhanced training, protocol updates, and machine learning tools to support clinical decision-making.} }
Endnote
%0 Conference Paper %T Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models %A Ariel Guerra-Adames %A Marta Avalos-Fernandez %A Océane Doremus %A Cédric Gil-Jardiné %A Emmanuel Lagarde %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-guerra-adames25a %I PMLR %P 420--439 %U https://proceedings.mlr.press/v259/guerra-adames25a.html %V 259 %X Judgment biases in emergency triage can adversely affect patient outcomes. This study examines sex/gender biases using four advanced language models fine-tuned on real-world emergency department data. We introduce a novel approach based on the testing method, commonly used in hiring bias detection, by automatically altering triage notes to change patient sex references. Results indicate a significant bias: female patients are assigned lower severity ratings than male patients with identical clinical conditions. This bias is more pronounced with female nurses or when patients report higher pain levels but diminishes with increased nurse experience. Identifying these biases can inform interventions such as enhanced training, protocol updates, and machine learning tools to support clinical decision-making.
APA
Guerra-Adames, A., Avalos-Fernandez, M., Doremus, O., Gil-Jardiné, C. & Lagarde, E.. (2025). Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:420-439 Available from https://proceedings.mlr.press/v259/guerra-adames25a.html.

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