A decomposition-based approach for multi-level appointment planning and scheduling

Area of Science:

• Operations Research
• Healthcare Management
• Biostatistics

Background:

• Population-based breast cancer screening programs face challenges with patient no-shows and variable service durations.
• Effective appointment scheduling is crucial for maximizing screening throughput and minimizing patient wait times.
• Existing scheduling models often struggle to account for stochastic patient behaviors and multi-level decision-making.

Purpose of the Study:

• To develop an integrated scheduling approach for breast cancer screening appointments.
• To optimize both tactical (patient assignment) and operational (appointment timing) decisions.
• To enhance screening program performance by considering patient-specific stochastic factors.

Main Methods:

• A decomposition method iterating between tactical and operational levels with feedback loops was developed.
• The tactical problem was reformulated as a deterministic mixed-integer quadratic-constrained programming problem.
• The operational problem utilized Sample Average Approximation and decomposed sequencing and timing decisions.

Main Results:

• The decomposition-based procedure demonstrated superior performance in terms of time and solution quality.
• The integrated approach effectively mitigated the impact of operational variability.
• Optimized scheduling led to improved screening numbers, reduced wait times, and better resource utilization.

Conclusions:

• The proposed decomposition method offers a robust solution for complex healthcare scheduling problems.
• This approach can significantly enhance the efficiency and effectiveness of population-based cancer screening.
• The findings provide valuable insights for optimizing resource allocation and patient flow in healthcare settings.