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关键议题，尤其在急诊分诊等高压医疗场景中，快速且公平的决策至关重要。流程挖掘正日益关注公平性问题，并逐步形成聚焦公平性感知算法的研究领域。然而，目前关于这些概念在实证医疗数据中的表现效果，以及它们对正义理论维度的覆盖程度，尚缺乏深入认知。本研究针对这一研究问题，提出一种将真实事件日志与正义概念维度相联结的流程挖掘方法，以评估分诊流程的公平性。我们基于MIMICEL事件日志（源自MIMIC-IV ED），将时间、返工、偏差和决策作为流程结果变量，并采用Kruskal-Wallis检验、卡方检验与效应量测量方法，评估年龄、性别、种族、语言和保险因素对其的影响。这些结果被映射至正义维度，以支持概念框架的构建。研究结果揭示了高风险与亚急性分诊场景中潜在不公平性的具体表征。通过实证分析，本研究为医疗领域负责任的公平性感知流程挖掘提供了经验性见解，有助于推动该方向的后续研究。