用流程挖掘分析新冠临床路径，发现救治瓶颈并优化医院管理
Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways
- 将异构临床数据转为可分析的事件日志，构建透明可复现的流程分析管道
- 发现急诊与住院交接处存在显著流程变异，重症监护影响患者预后
- 适合医疗管理者、临床流程优化人员参考，助力医院精细化运营
本研究基于《COVID Data for Shared Learning》数据集，分析新冠患者临床路径。构建了透明、可复现的数据处理流程，将异构临床表转化为流程挖掘可用的事件日志，并开展流程发现、声明式合规检查与结果分析。重构的临床路径揭示了住院治疗中的监测核心作用，急诊-入院接口处的流程变异，以及年龄和是否接受重症监护对治疗结果的影响。这些发现有助于标准化分诊流程、优化床位资源配置，以及加强重症到低强度病房的转科协调，展示了流程挖掘在支持循证医院治理中的价值。
原文摘要 · Abstract (English)
This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transforms heterogeneous clinical tables into a process-mining-ready event log and applies discovery, declarative conformance checking, and outcome analysis. The reconstructed pathways highlight the monitoring backbone of inpatient care, variability at the Emergency department-admission interface, and outcome differences driven by age and exposure to intensive care units. These insights support triage standardization, capacity planning, and step-down coordination from intensive care units to lower-acuity wards, showing how process mining can inform evidence-based hospital governance.
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