A Novel Workload Manager System (WORMS) to Evenly Distribute Patient-Specific Clinical Tasks Among the Physics Team
Abstract
Purpose
A Novel system to distribute evenly and efficiently patient-specific daily, quarterly and yearly clinical workload was developed and optimized to reduce risk of burnout and create space for non-clinical activities.
Methods
Approximately 7,000 patient-specific clinical tasks are sent to the Physics Team to complete per year (~130-140 tasks/week). Each task takes, on average, 40min+/-10min to be completed. A weighted rostering allocation (1RA=1day), depending on general or specialized duty, is assigned to each physicist to cover the patient-specific clinical tasks. An online system (web-application) was developed in house to assign and track the roster allocation per physicist and record the tasks completed by each physicist when rostered. Physicists pre-schedule their leave in the system two months in advance and then rostering allocations are assigned to them through the system based on their availability and specialty. Physicists record in the system every task completed during the day. To help balance the daily and weekly workload, the system displays in real-time the daily tasks/physicist, along with the average.
Results
This novel system facilitates the even distribution of patient-specific general clinical tasks on daily and weekly-basis. Physicists assigned to general clinical duty (4 physicists/day) complete ~6-7 tasks/day. This model allows rostered physicists to perform other non-clinical and non-urgent clinical work. The total RA assignment is equivalent to 45% ±1% of the work calendar-days and permits roughly 55% of the remaining work calendar-days to be invested in physicist-specific portfolios (QA, leadership), research/academic and vacation.
Conclusion
The novel clinical distribution model allows the patient-specific workload to be distributed evenly among the Physics Team regardless the treatment modality and/or expertise/experience of rostered physicists. The model adapts for daily, quarterly and/or yearly workload fluctuations. It is dynamic and scalable but most importantly, it is collaborative and transparent, and reduces the risk of mental fatigue, and thus burnout.