A data-driven approach to solve the RT scheduling problem.

Introduction: There is an increase in demand for Radiotherapy (RT) and it is a time critical treatment with a complex scheduling process. RT workflow is inter-dependent and involves various steps including pre-treatment and treatment-related tasks which adds to these challenges. Globally, scheduling delays are reported as one of the most common issues in RT. We aim to create and evaluate an automated strategy which generates a patient allocation list to assist the scheduling staff to create an efficient scheduling process. Methods and Materials: We used historical data from a large RT department in Sweden from January to December 2022 with 11-13 operational linear accelerators. The algorithm was developed in C# language. It utilizes patient and treatment-related characteristics including the patient timeline (referral date, preferred treatment start dates), booking category, diagnosis group and intent. Based on this, the algorithm assigns patient priority individually. Results: The algorithm's output resulted in a scheduling list sorted by high to low patient priority per week. We evaluated the algorithm with historical manual allocations from the same year. The comparison between manual and algorithm allocations showed that the number of delayed patients reduced by 10 % in the algorithm suggestion with an average delay reduction of 2 weeks. Furthermore, the focus on patient-related characteristics resulted in diagnosis groups being better balanced. Conclusion: The algorithm's ability to produce quick results may save significant time that the scheduling staff otherwise need to assess individual patient profiles. RT departments can incorporate such algorithms to accelerate their scheduling decisions and enhance their overall scheduling performance before going through major organizational changes.