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

2026-01-16

AI Summary

This study addresses fairness in automated decision-making within emergency triage, particularly through the lens of multidimensional justice theory in high-pressure clinical settings. It pioneers the integration of process mining with justice theory to systematically analyze potential biases in the MIMIC-EL event log across demographic variables—including age, sex, race, language, and insurance status. By employing process mining techniques alongside Kruskal-Wallis tests, chi-square tests, and effect size measures, the research quantifies inequities across key process dimensions such as time, rework, deviation, and decision outcomes. The findings reveal systematic disparities between high-acuity and lower-acuity triage pathways, offering both empirical evidence and theoretical grounding for developing responsible, fairness-aware approaches to healthcare process mining.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• fairness
• healthcare triage
• process mining
• justice theory
• automated decision-making

Innovation

• fairness-aware process mining
• healthcare triage
• justice theory
• empirical fairness analysis
• MIMIC-IV ED