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.

翻译：自动化决策中的公平性已成为一个关键问题，特别是在急诊分诊等高压力医疗场景中，快速且公平的决策至关重要。过程挖掘领域正日益关注公平性问题，专注于公平感知算法的研究领域也在不断扩大。迄今为止，我们对于这些概念在实证医疗数据上的表现如何，或它们如何覆盖正义理论的各个方面仍知之甚少。本研究针对这一研究问题，提出了一种过程挖掘方法，通过将真实事件日志与正义的概念维度相关联来评估分诊中的公平性。利用MIMICEL事件日志（源自MIMIC-IV ED），我们分析了时间、重复操作、偏差和决策作为流程结果，并使用Kruskal-Wallis检验、卡方检验及效应量测量评估了年龄、性别、种族、语言和保险因素的影响。这些结果被映射到正义维度，以支持概念框架的构建。研究结果揭示了在高危急症和亚急性情况下潜在不公平性的具体表现方面。通过这种方式，本研究提供了实证见解，为医疗领域负责任、公平感知的过程挖掘的进一步研究提供了支持。