发表机构
University of Milan（米兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
通过过程挖掘分析急诊科事件日志，发现流程瓶颈、变体碎片化及优先级倒置，并提出基于 acuity 的护理路径和预期性出院两种数据驱动的重新设计方案。
AI 中文摘要
急诊科（EDs）运行着医疗保健中标准化程度最低的一些流程，而长时间停留往往被归因于需求而非基于数据进行测量。我们通过一个完整的过程挖掘管道分析了一个包含1,820次急诊科停留（25,115个事件）的匿名事件日志：预处理解决了突发日志记录和缺失值问题，性能分析在案例和转换层面量化了流程，归纳挖掘器结合基于令牌的符合性检查评估了流程结构。过滤后的日志（1,754个案例，16,376个事件）显示出平均吞吐时间为6.58小时且具有重尾分布，有884个不同的控制流变体，其中最频繁的变体仅覆盖4.3%的案例，并且存在临床优先级倒置，即紧急患者（ acuity 2，8.03小时）的停留时间比危重患者（acuity 1，5.83小时）长38%。完美拟合度（1.0）结合低精确度（0.71）揭示了规范路径的缺失而非偏差。基于 acuity 的护理路径和预期性出院这两个重新设计方案，带有量化目标被提出。
英文摘要
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.
