Data-Driven Two-stage Appointment Radiotherapy Scheduling Model for Resource Optimization at a Tertiary Cancer Center

Abstract

Abstract Background: The timely delivery of radiotherapy (RT) is crucial to cancer care, and excessive delays in RT have been associated with detrimental oncological and psychological outcomes. Prior to receiving RT on treatment units (Linear accelerators), there are a few processes that need to take place including simulation (on CT simulators), radiotherapy plan generation/optimization and quality assurance. The assignment of patient schedules on CT simulators and Linear accelerators is currently done manually at most cancer centers. We propose that data-driven optimization of patient scheduling has the potential to improve wait-times, and optimize use of departmental resources. Methods: A two-stage Mixed Integer Programming model was developed to optimize the patient appointment scheduling process and to forecast machine utilization. The model was tested with historical institutional data from Princess Margaret Cancer Center. By analyzing the dataset and simulating historical patient arrivals, the model output is evaluated by comparing patient wait time statistics and monthly machine utilization against what occurred during this time frame. Results: Testing our model on data from 2019-06 to 2020-02, we found a reduction in average wait time from 11.2 to 6.7 business days for standard category patients. The number of standard patients exceeding the wait time target of 10 business days were reduced from 118 to 15 patients each month. In addition, our model could accurately estimate future machine utilization for both CT simulators and linear accelerators based on the model output appointments, which could facilitate better planning and utilization of departmental resources. Conclusion: Our scheduling model has the potential to reduce the standard patient wait time for radiation treatment without compromising the wait time for urgent patients. The model can be also used to forecast department resources and machine utilization based on the output of the scheduling model. Radiotherapy departments could use this model to generate patient appointment schedules as well as to reduce machine idle time or appointment over-booking.