Levelling bed occupancy: reconfiguring surgery schedules via simulation.

Area of Science:

• Operations Research
• Healthcare Management
• Simulation Modelling

Background:

• Effective hospital operational planning requires optimizing patient flow and resource allocation.
• Master surgery schedules (MSS) significantly impact hospital bed occupancy and overall efficiency.
• Hong Kong hospitals face challenges in managing capacity and ensuring smooth patient journeys.

Purpose of the Study:

• To apply simulation modelling to reconfigure a 700-bed Hong Kong hospital's master surgery schedule (MSS).
• To improve patient flow, capacity management, and resource allocation by levelling bed occupancy.
• To provide actionable business intelligence for short- and long-term hospital planning.

Main Methods:

• Development of a discrete-event simulation model to analyze MSS changes and their effect on bed occupancy.
• Creation of a decision tool for hospital managers to test various scheduling scenarios.
• Utilisation of optimisation routines within the simulation model for long-term schedule improvements.

Main Results:

• Simulation demonstrated that minor, practical adjustments to the MSS can achieve significant bed occupancy levelling.
• Optimisation routines provided further insights for long-term master surgery schedule revamp.
• The model successfully evaluated the relationship between the MSS and bed occupancy.

Conclusions:

• Operations research methods provide a robust framework for guiding hospital operational planning.
• A data-driven, evidence-based simulation model empowers managers to explore scheduling changes effectively.
• This advanced analytics approach facilitates informed decision-making and enhances efficiency and service quality in Hong Kong hospitals.