Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage

TL;DR

This paper presents a process mining approach to evaluate fairness in healthcare triage decisions using real event logs and justice theory, revealing potential biases related to demographic factors.

Contribution

It introduces a novel method linking process mining with justice dimensions to empirically assess fairness in healthcare triage.

Findings

Identifies demographic biases in triage outcomes

Maps unfairness aspects to justice theory

Provides empirical insights for fairness-aware process mining

Abstract

Fairness in automated decision-making has become a critical concern, particularly in high-pressure healthcare scenarios such as emergency triage, where fast and equitable decisions are essential. Process mining is increasingly investigating fairness. There is a growing area focusing on fairness-aware algorithms. So far, we know less how these concepts perform on empirical healthcare data or how they cover aspects of justice theory. This study addresses this research problem and proposes a process mining approach to assess fairness in triage by linking real-life event logs with conceptual dimensions of justice. Using the MIMICEL event log (as derived from MIMIC-IV ED), we analyze time, re-do, deviation and decision as process outcomes, and evaluate the influence of age, gender, race, language and insurance using the Kruskal-Wallis, Chi-square and effect size measurements. These outcomes are mapped to justice dimensions to support the development of a conceptual framework. The results demonstrate which aspects of potential unfairness in high-acuity and sub-acute surface. In this way, this study contributes empirical insights that support further research in responsible, fairness-aware process mining in healthcare.