Simple Heuristics for Near-Optimal Appointment Scheduling in Primary Care

Area of Science:

• Operations Research in Healthcare
• Primary Care Management
• Health Services Research

Background:

• Current appointment scheduling in primary care often leads to suboptimal resource allocation.
• Practices frequently prioritize larger issues over optimizing appointment schedules.
• Implementing advanced scheduling solutions can be resource-intensive.

Purpose of the Study:

• To compare the performance of simple scheduling heuristics against optimal schedules.
• To evaluate the effectiveness of the 'First Minimum' heuristic for appointment scheduling.
• To assess the feasibility of implementing scheduling improvements in primary care settings.

Main Methods:

• Simulation of simple heuristics versus offline optimization models for appointment scheduling.
• Utilized uncapacitated appointment calendars representing a nationally representative primary care panel.
• Analyzed stochastic appointment request distributions to determine optimal capacity.

Main Results:

• The 'First Minimum' heuristic demonstrated near-optimal scheduling performance.
• Optimal schedules were identified using offline optimization models.
• Stochastic modeling provided insights into daily appointment distributions and optimal capacity.

Conclusions:

• The 'First Minimum' heuristic is a practical and effective method for improving primary care appointment scheduling.
• This heuristic can be implemented with minimal resources, even using pen-and-paper methods.
• Optimizing appointment scheduling can enhance patient access and practice efficiency without significant technological investment.