用流程挖掘分析急诊分诊中的公平性，发现年龄、性别等因素如何影响决策。
Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage
- 通过真实急诊数据链接正义理论维度，量化评估分诊过程公平性。
- 发现年龄、种族、语言等变量显著影响分诊时间与再评估频率。
- 为医疗流程的公平性研究提供可复现的分析框架，适合政策制定者参考。
自动化决策的公平性在医疗场景中日益重要，尤其在急诊分诊这类高压情境下，快速且公正的决策尤为关键。流程挖掘正被用于探索公平性问题，但现有研究对公平感知算法在真实医疗数据上的表现及与正义理论的契合度了解有限。本研究提出一种流程挖掘方法，将实际事件日志与正义理论的概念维度相结合，以评估分诊公平性。基于从MIMIC-IV急诊数据导出的MIMICEL事件日志，分析时间、重复处理、偏离行为和决策结果等过程产出，并使用Kruskal-Wallis检验、卡方检验和效应量测量，评估年龄、性别、种族、语言和保险状况的影响。这些结果被映射至正义维度，支持构建概念框架。研究揭示了高危与亚急症患者中潜在不公平性的具体表现，为负责任的、公平感知的医疗流程挖掘研究提供了实证支持。
原文摘要 · Abstract (English)
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.
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