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

TL;DR

The paper addresses fairness in automated triage decisions by integrating fairness‑aware process mining with organizational justice theory. Using the MIMIC‑IV Emergency Department dataset converted to an event log (MIMICEL), the authors examine four process outcomes—time, re‑do, deviation, and discharge decision—and analyze how demographics such as age, race, gender, insurance, and language associate with these outcomes using nonparametric tests. By mapping outcomes to distributive, procedural, and interactional justice, they provide an empirical framework that links data‑driven process insights to ethical concepts, revealing disparities particularly among higher acuity and sub‑acute cases and across insurance and language groups. The study contributes a structured methodology for fairness‑aware process mining in healthcare and offers guidance for monitoring and redesigning triage workflows to promote equitable care in practice.

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.

Figures (3)

• Figure 1: ESI Triage Process as BPMNgilboy2012emergency
• Figure 2: Data Processing Pipeline for Fairness Analysis in Triage
• Figure 3: MIMIC-IV ED