Scheduling admissions and reducing variability in bed demand.

Area of Science:

• Healthcare Operations Research
• Queueing Theory Applications
• Hospital Bed Management

Background:

• Variability in patient admissions and lengths of stay directly impacts hospital bed occupancy rates.
• Unpredictable occupancy patterns pose challenges for efficient hospital capacity planning and resource allocation.

Purpose of the Study:

• To analyze how variability in admissions and length of stay affects required hospital capacity.
• To develop a method for determining admission quotas for scheduled admissions to regulate occupancy patterns.
• To provide practical guidelines for hospital managers to optimize bed management.

Main Methods:

• Utilized a heavy-traffic limit theorem for the G/G/∞ queue to approximate the impact of variability on bed requirements, especially for non-Poisson arrival processes.
• Applied time-dependent analysis to determine the mean offered load per day, considering structural weekly admission patterns.
• Integrated a Quadratic Programming model with time-dependent analysis to determine optimal daily elective admission numbers for achieving desired average occupancy.

Main Results:

• Developed an approximation for required hospital beds considering variability in arrivals and service times.
• Established a method to calculate daily offered load based on weekly admission patterns.
• Optimized elective admission numbers to achieve target daily bed occupancy rates.

Conclusions:

• The study provides a data-driven approach to hospital capacity management through admission quota scheduling.
• Mathematical models and practical guidelines are derived to help hospital managers regulate occupancy and improve resource utilization.
• Implementation of admission quotas can lead to more predictable bed occupancy and efficient healthcare operations.