Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways

Researchers applied process mining techniques to COVID-19 clinical data to optimize hospital workflow management, revealing variability in emergency department procedures and identifying outcome differences based on patient age and ICU exposure. The study demonstrates how data-driven process analysis can inform evidence-based hospital governance and resource allocation.

This research applies computational process mining—a data science discipline that reconstructs and analyzes business workflows—to clinical healthcare operations during the COVID-19 pandemic. The study transforms raw clinical data into structured event logs, enabling hospitals to visualize actual patient pathways rather than relying on theoretical protocols. This approach identifies critical bottlenecks at the emergency department-to-admission interface, where significant operational variability exists, and uncovers how patient outcomes correlate with age demographics and intensive care unit exposure patterns.

Process mining represents an emerging frontier in healthcare optimization, moving beyond traditional quality improvement methods by providing granular, data-driven visibility into complex clinical workflows. Healthcare systems generate enormous volumes of transactional data through electronic health records, yet most institutions lack systematic mechanisms to analyze these patterns at scale. This study bridges that gap by establishing a reproducible pipeline, enabling other hospitals to adopt similar analytical frameworks.

For hospital administrators and healthcare policy makers, these insights directly impact operational efficiency and patient outcomes. Triage standardization based on empirical pathway data reduces unnecessary bottlenecks. Capacity planning becomes more precise when informed by actual patient flow patterns rather than assumptions. ICU step-down coordination improvements translate to reduced length of stay and better resource utilization during surges. The methodology proves particularly valuable during future pandemic preparedness, where understanding care pathway variants across patient populations informs surge planning.

Institutions implementing process mining analytics gain competitive advantages in patient safety metrics and operational efficiency. As healthcare systems increasingly digitize records, process mining tools will likely become standard governance infrastructure, similar to financial auditing in other industries.

• →Process mining transforms clinical data into actionable workflow insights, identifying operational bottlenecks at emergency department-admission interfaces.
• →Patient outcomes vary significantly based on age and ICU exposure patterns, revealing optimization opportunities for triage protocols.
• →The reproducible pipeline methodology enables healthcare systems to systematically analyze and improve clinical pathway efficiency.
• →Data-driven insights support evidence-based capacity planning and resource allocation during patient surges.
• →Process mining establishes a scalable framework for pandemic preparedness and ongoing hospital governance optimization.