Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways
For hospital administrators and clinicians, it demonstrates how process mining can inform triage standardization and capacity planning, though the approach is incremental.
This study analyzes COVID-19 care pathways using process mining on a clinical dataset, revealing variability in emergency-to-admission transitions and outcome differences by age and ICU exposure, supporting evidence-based hospital management.
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.