A Monte Carlo computer model to investigate patient scheduling

Area of Science:

• Operations Research
• Healthcare Management
• Health Informatics

Background:

• Optimizing patient flow in clinics is crucial for efficient healthcare delivery.
• Appointment scheduling significantly impacts physician utilization and patient waiting times.
• Understanding no-show rates and walk-ins is vital for effective clinic management.

Purpose of the Study:

• To investigate the impact of different patient appointment scheduling strategies on clinic efficiency.
• To evaluate the trade-offs between physician utilization and patient waiting times.
• To identify optimal scheduling models for single-channel healthcare queues.

Main Methods:

• Developed a Monte Carlo computer simulation model.
• Incorporated parameters such as no-show incidence, walk-in rates, and physician examination time distributions.
• Simulated five different hospital outpatient clinics using various appointment scheduling methods.

Main Results:

• The simulation generated frequency distributions for physician utilization and total patient waiting times.
• Analysis revealed that schedules with even increments between appointments performed optimally.
• Even increment schedules demonstrated the best balance between maximizing physician utilization and minimizing patient delays.

Conclusions:

• Even increment appointment scheduling is a highly effective strategy for improving clinic efficiency.
• This scheduling approach can lead to better resource allocation and enhanced patient satisfaction.
• The Monte Carlo model provides a valuable tool for evaluating and optimizing healthcare scheduling systems.