COMP Report: An updated algorithm to estimate medical physics staffing levels for radiation oncology

Abstract

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.

Derivation of a Unique, Algorithm-Based Approach to Cancer Patient Navigator Workload Management

“…To our knowledge, this work may represent the first-ever description of an automated, algorithm-driven approach to even out CPN workloads. Optimization has been applied to health care staffing and patient allocation in other health care domains, 22 - 25 but this is usually applied to shifts rather than individuals. Previous work has demonstrated that lay patient navigators may accurately predict the work intensity needed for a patient.…”

A system for equitable workload distribution in clinical medical physics

“…This was a key step because a different interpretation or understanding of the task can lead to variability in time estimates from different physicists. Survey data were collected from a range (2)(3)(4)(5)(6)(7)(8)(9)(10)(11) of people who were familiar with each task in order to reduce bias in the estimates. The total number of patients who underwent special procedures (e.g., intravascular brachytherapy) was extracted from the Oncology Information System Mosaiq (Elekta Inc.; Crawley, UK) through database queries.…”

“…Without a meaningful metric for clinical effort,discussions of staffing and workload become difficult and it is very challenging to ensure equity in terms of the distribution of clinical tasks. Only a few approaches to account for the clinical effort and variability in medical physics have been described in the literature, [5][6][7] and they mainly focus on staffing levels and scheduling rather than workload assignment. On the other hand, several studies in nonclinical academic environments have shown that heavier workloads in tasks such as teaching, administrative service, and mentoring can cause a gender or racial disparity regarding promotion.…”