🤖 AI Summary
This study addresses the lack of standardization in emergency department (ED) workflows, where patient boarding is frequently attributed to demand based on subjective assumptions rather than empirical evidence. Leveraging end-to-end process mining techniques on ED event logs, this work employs inductive process discovery, token-based conformance checking, and data preprocessing to quantify flow performance and assess structural consistency. The analysis identifies 884 control-flow variants, revealing that a model exhibiting perfect fitness yet low precision indicates the absence of normative pathways. Notably, it uncovers a previously undocumented clinical priority inversion wherein urgent patients experience longer lengths of stay than critical patients. Based on these findings, two process improvement strategies are proposed, providing an evidence-based foundation for optimizing ED management.
📝 Abstract
Emergency departments (EDs) run some of the least standardized processes in healthcare, and long stays are often attributed to demand rather than measured on data. We analyze an anonymised event log of 1{,}820 ED stays (25{,}115 events) through a full process mining pipeline: preprocessing resolves burst logging and missing values, performance analysis quantifies flow at the case and transition level, and Inductive Miner with token-based conformance checking assesses process structure. The filtered log (1{,}754 cases, 16{,}376 events) exhibits a mean throughput of 6.58 hours with a heavy tail, 884 distinct control-flow variants whose most frequent one covers only 4.3\% of cases, and an inversion of clinical priority in which urgent patients (acuity 2, 8.03 h) stay 38\% longer than critical ones (acuity 1, 5.83 h). Perfect fitness (1.0) combined with low precision (0.71) reveals absence of normative pathways rather than deviance. Two redesign scenarios, acuity-based care pathways and anticipatory discharge, are derived with quantified targets.