Optimizing PICU Resource Management: A Data-Driven Discrete Event Simulation Approach for Capacity and Flow Modeling

Area of Science:

• Healthcare Operations Research
• Biomedical Informatics
• Systems Engineering

Background:

• Hospitals require advanced tools for optimizing resource utilization and patient flow.
• Data-driven insights are essential for improving hospital management.
• Existing models may not fully capture the complexities of multiunit patient flow.

Purpose of the Study:

• To develop and evaluate a flexible, data-driven discrete event simulation (DES) model.
• To optimize capacity utilization and patient flow in a multiunit hospital system, focusing on the Pediatric Intensive Care Unit (PICU) and downstream units.
• To provide a tool for enhancing hospital operational decision-making.

Main Methods:

• Retrospective discrete-event simulation modeling and validation study.
• Utilized historical patient admission data from a quaternary referral hospital (Boston Children's Hospital, January 2012 - February 2025).
• Validated the model against a real-world PICU expansion scenario.

Main Results:

• The DES model accurately predicted post-expansion PICU length of stay and capacity utilization when downstream unit capacities were incorporated.
• Simulations highlighted the critical impact of downstream bottlenecks on overall patient flow and resource utilization.
• The model demonstrated utility for capacity planning and optimizing new service line scheduling using synthetic data.

Conclusions:

• The open-source DES model effectively simulates patient flow across multiple hospital units.
• It offers a powerful, flexible tool for administrators to optimize hospital operations and resource allocation.
• The model is transferable and adaptable to various healthcare systems and scenarios.