Smart Scheduling: A Pilot Project of Workforce Scheduling in Radiation Oncology

Abstract

Introduction: In the case of the radiation oncology department, the large number of visits faced by patients illuminates the critical need for optimal time management. Focused on three central themes: daily waiting times, diagnosis-to-treatment waiting times, and appropriate staffing for the present workload, the research highlights the impact of inefficient time management on patient satisfaction and overall operational efficiency. The time and energy invested in a schedule are high and frequently many scheduling conflicts occur even after the schedule is made. The ability to schedule different employees in the most optimal manner would increase the productivity of the radiation oncology department. Methods: The scheduling software was constructed using Python language and importations of libraries from the Tkinker software for the Graphical User Interface. The software is a constraint-based algorithm that allocates staff to different sites based on each radiation therapy clinic’s staffing requirements. Results and Discussion: This work developed a basic software that creates a randomized schedule of employees. While this would benefit the team by curating a schedule that has no functional mistakes, the algorithm provides a foundation for the data collection that will facilitate the future incorporation of artificial intelligence (AI). This would allow for deeper learning overtime of the software to develop a schedule that is optimal for the success of the individual and, thus, the entire team. This pilot project aimed to generate interest regarding the introduction of AI to current scheduling software in the context of the radiation oncology department.

Keywords

• workforce scheduling
• radiation oncology
• time management
• patient satisfaction
• operational efficiency
• clinical workflow
• constraint-based scheduling
• optimization algorithms