Stochastic Simulation for the Optimisation of MRI and CT Operations in a Paediatric Public Hospital

Area of Science:

• Healthcare Operations Research
• Medical Imaging Informatics
• Public Health Systems Engineering

Background:

• Radiology departments face challenges optimizing services for complex pediatric populations.
• Variability in patient demand and staffing limitations impact operational efficiency.
• Stochastic simulation offers a powerful tool for addressing these complexities.

Purpose of the Study:

• To apply G/G/c stochastic simulation to analyze MRI and CT services in a Greek pediatric hospital.
• To identify critical factors contributing to waiting times and congestion.
• To provide a decision-support foundation for operational redesign in radiology departments.

Main Methods:

• A case-based study utilizing doctoral dissertation research on MRI and CT services.
• Implementation of a G/G/c simulation model in Python.
• Analysis through Monte Carlo scenarios to evaluate operational performance.

Main Results:

• Waiting times and congestion are influenced by scanner availability, emergency demand, anesthesia dependency, and staffing.
• The simulation model effectively captured real-world operational conditions.
• Identified specific bottlenecks and their impact on patient throughput.

Conclusions:

• Stochastic simulation provides a robust framework for understanding and optimizing radiology department operations.
• The findings support evidence-based decision-making for operational redesign.
• Simulation modeling is crucial for improving efficiency in pediatric radiology services.