Reallocation of chemotherapy appointments in a large health system using a mixed integer linear programming approach

Area of Science:

• Health Systems Science
• Operations Research
• Oncology Patient Care

Background:

• Outpatient chemotherapy scheduling significantly impacts patient satisfaction and health system resource utilization.
• Destination medical centers often face high appointment volumes, creating resource strain.
• Patient travel distance is a key factor influencing treatment location preference and scheduling decisions.

Purpose of the Study:

• To develop and evaluate a model for reallocating outpatient chemotherapy appointments to optimize health system resource utilization.
• To assess the potential for decompressing appointment volume at a primary treatment site by utilizing other system locations.
• To analyze the impact of patient travel distance on appointment reallocation strategies.

Main Methods:

• A Mixed Integer Linear Programming (MILP) model was developed to reallocate appointments.
• The model considered four distinct scenarios for appointment redistribution.
• The approach was applied to real-world data from the Mayo Clinic Health System Minnesota region.

Main Results:

• The study demonstrated the potential to move approximately 50% of eligible outpatient chemotherapy appointments from the primary site.
• This reallocation resulted in an approximate 30% overall change in appointment volume distribution.
• Leveraging underutilized sites effectively utilizes oncology nurses and chair availability.

Conclusions:

• Reallocating outpatient chemotherapy appointments can significantly improve efficiency and resource management within health systems.
• Integrating patient travel distance into scheduling models is crucial for successful appointment redistribution.
• This approach offers a viable strategy for optimizing chemotherapy delivery and patient access across multiple sites.