Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage
It addresses fairness in automated decision-making for healthcare processes, providing empirical insights but is incremental as it applies existing methods to new data.
This study tackled the problem of assessing fairness in emergency triage decision-making by linking real-life healthcare event logs with justice theory dimensions, analyzing factors like age and race to identify potential unfairness in high-acuity and sub-acute cases.
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.