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Improving organizational processes in healthcare through simulation-driven resource allocation: Methodology and
Francesco Vinci1, Davide Aloini2, Elisabetta Benevento2
1Department of Mathematics, University of Padua, Padua, 35121, Italy.
Artificial Intelligence in Medicine
|March 11, 2026
Summary
Improving healthcare efficiency requires optimizing processes. This study uses process mining and simulation to data-drive resource allocation, significantly reducing patient waiting times in emergency departments.
Area of Science:
- Healthcare Management
- Operations Research
- Data Science
Background:
- Aging populations strain healthcare systems, necessitating improved efficiency and effectiveness.
- Healthcare delivery relies on complex processes governed by regulations and protocols.
- Traditional methods for process analysis have limitations in complex healthcare environments.
Purpose of the Study:
- To introduce a novel methodology for improving healthcare organizational processes using data-driven resource allocation.
- To leverage process mining and simulation for creating realistic models of healthcare operations.
- To enhance the efficiency and effectiveness of healthcare systems through objective, data-driven insights.
Main Methods:
- Utilizing process mining to discover objective, data-driven simulation models from transactional system data.
- Employing simulation modeling to analyze and evaluate alternative 'what-if' scenarios for process improvement.
- Applying the methodology to an emergency department to focus on reducing patient waiting times.
Main Results:
- Demonstrated a potential substantial reduction in patient waiting times (88%) through data-driven resource allocation.
- Showcased a modest cost increase (7%-8%) associated with the proposed improvements.
- Highlighted enhanced process resilience to patient arrival surges, improving emergency preparedness.
Conclusions:
- Process mining and simulation offer a powerful, data-driven approach to optimize healthcare processes.
- The methodology enables realistic modeling and testing of improvements without disrupting live operations.
- Optimized healthcare processes lead to significant reductions in waiting times and improved system resilience.
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