COMP Report: An updated algorithm to estimate medical physics staffing levels for radiation oncology
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PURPOSE: Medical physics staffing models require periodic review due to the rapid evolution of technology and clinical techniques in radiation oncology. We present an update to a grid-based physics staffing algorithm for radiation oncology (originally published in 2012) that has been widely used in Canada over the last decade. MATERIALS AND METHODS: The physics staffing algorithm structure was modified to improve the clarity and consistency of input data. We collected information on clinical procedures, equipment inventory, and teaching activities from 15 radiation treatment centers in the province of Ontario from April 1, 2018, to March 31, 2019. Using these data sets, the algorithm's weighting parameters were adjusted to align the prediction of full-time equivalent (FTE) personnel with actual staffing levels in Ontario. The algorithm computes FTE estimates for medical physicists, physics assistants, engineering (electrical and mechanical), and information technology (IT) support. The performance of the algorithm was also tested in eight Canadian cancer centers outside of Ontario. RESULTS: The mean difference between the algorithm and actual staffing for the 23 Canadian cancer centers did not exceed 0.5 FTE for any staffing group. The results were slightly better in Ontario than in other provinces, as expected since the algorithm was optimized using Ontario data. There was a linear correlation between the algorithm predictions and the number of annual-treated cases for physicists, and physicists plus physics assistants. For other staff categories, the algorithm weighting parameters were not significantly altered, except for a reduction in mechanical engineering staff. Comparison with other published models suggests that the updated algorithm should be considered as a minimum recommended staffing level for the clinical support of radiation oncology programs. CONCLUSIONS: We support the use of grid-based physics staffing algorithms that account for clinical workload with flexibility to adapt to local conditions with variable academic and research demands.
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