[2601.11065] Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage

Computer Science > Computers and Society arXiv:2601.11065 (cs) [Submitted on 16 Jan 2026 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage Authors:Rachmadita Andreswari, Stephan A. Fahrenkrog-Petersen, Jan Mendling View a PDF of the paper titled Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triage, by Rachmadita Andreswari and 2 other authors View PDF HTML (experimental) 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. ...