Discrimination, understood as the unjust or prejudicial treatment of individuals based on characteristics such as age, gender, race, or disability, is a deeply contested issue and a significant challenge for contemporary society. This paper examines potential instances of discrimination within the healthcare sector, with particular focus on the triage process. We employ causal networks and causal mediation analysis to answer two key questions: (1) Does triage decision-making exhibit disparity based on demographic factors such as race, gender, or age? (2) If such disparities are identified, to what extent can they be attributed to clinically relevant factors rather than to demographic characteristics? The results reveal demographic disparities in triage assignments for age, gender, and race. However, causal mediation analysis shows that gender and race disparities are entirely explained by clinical mediators. In contrast, age exhibits a direct effect on acuity scores beyond its influence on vital signs, though this may reflect clinically appropriate age-based triage protocols rather than unjustified discrimination. These findings highlight the importance of causal analysis in distinguishing between statistical disparities and actual discrimination in healthcare settings.

Armanini, J., Stella, F. (2026). Triage Discrimination: Myth or Reality?. In Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning First International Joint Conference, HC@AIxIA+HYDRA 2025, Bologna, Italy, October 25–26, 2025, Proceedings (pp.352-365). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-032-16708-8_29].

Triage Discrimination: Myth or Reality?

Armanini J.;Stella F.
2026

Abstract

Discrimination, understood as the unjust or prejudicial treatment of individuals based on characteristics such as age, gender, race, or disability, is a deeply contested issue and a significant challenge for contemporary society. This paper examines potential instances of discrimination within the healthcare sector, with particular focus on the triage process. We employ causal networks and causal mediation analysis to answer two key questions: (1) Does triage decision-making exhibit disparity based on demographic factors such as race, gender, or age? (2) If such disparities are identified, to what extent can they be attributed to clinically relevant factors rather than to demographic characteristics? The results reveal demographic disparities in triage assignments for age, gender, and race. However, causal mediation analysis shows that gender and race disparities are entirely explained by clinical mediators. In contrast, age exhibits a direct effect on acuity scores beyond its influence on vital signs, though this may reflect clinically appropriate age-based triage protocols rather than unjustified discrimination. These findings highlight the importance of causal analysis in distinguishing between statistical disparities and actual discrimination in healthcare settings.
paper
Causal Networks; Discrimination; Mediation Analysis; Triage;
English
First International Joint Conference, HC@AIxIA+HYDRA 2025 - October 25–26, 2025
2025
Bruno, P; Calimeri, F; Cauteruccio, F; Dragoni, M; Stella, F; Terracina, G
Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning First International Joint Conference, HC@AIxIA+HYDRA 2025, Bologna, Italy, October 25–26, 2025, Proceedings
9783032167071
2026
2830
352
365
none
Armanini, J., Stella, F. (2026). Triage Discrimination: Myth or Reality?. In Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning First International Joint Conference, HC@AIxIA+HYDRA 2025, Bologna, Italy, October 25–26, 2025, Proceedings (pp.352-365). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-032-16708-8_29].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/614802
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