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

A national survey of medical staffs' required capability and workload for accelerator-based boron neutron capture therapy

This study aimed to identify the required capabilities and workload of medical staff in accelerator-based boron neutron capture therapy (BNCT). From August to September 2022, a questionnaire related to the capabilities and workload in the accelerator-based BNCT was administered to 12 physicians, 7 medical physicists and 7 radiological technologists engaged in BNCT and 6 other medical physicists who were not engaged in BNCT to compare the results acquired by those engaged in BNCT. Only 6-21% of patients referred for BNCT received it. Furthermore, 30-75% of patients who received BNCT were treated at facilities located within their local district. The median required workload per treatment was 55 h. Considering additional workloads for ineligible patients, the required workload reached ~1.2 times longer than those for only eligible patients' treatment. With respect to capabilities, discrepancies were observed in treatment planning, quality assurance and quality control, and commissioning between medical physicists and radiological technologists. Furthermore, the specialized skills required by medical physicists are impossible to acquire from the experience of conventional radiotherapies as physicians engaged in BNCT were specialized not only in radiation oncology, but also in other fields. This study indicated the required workload and staff capabilities for conducting accelerator-based BNCT considering actual clinical conditions. The workload required for BNCT depends on the occupation. It is necessary to establish an educational program and certification system for the skills required to safely and effectively provide BNCT to patients.

Quantitative Assessment of Full-Time Equivalent Effort for kV-CBCT Guided Online Adaptive RT for Medical Physicists

INTRODUCTION

With recent clinical adoption of online adaptive radiotherapy and the increased workload associated with adaptive radiotherapy, proper staffing for medical physicists is paramount to safe clinical operation. However, there is currently no consensus on the full-time equivalent (FTE) requirements for safe administration of CBCT-guided online adaptive radiotherapy. This study aims to quantitatively assess medical physics workload and staffing needs of a CBCT-guided online adaptive radiotherapy program.

METHODS

We conducted a detailed analysis of the CBCT-guided adaptive planning and treatment workflows, encompassing tasks such as patient consultation, treatment planning, plan review, training, quality assurance, and treatment delivery. Utilizing data from machine logs, clinical database queries, and staff surveys, we present a framework for estimating FTE values for different staffing scenarios, considering medical physicists' roles as planners, adaptors, or both.

RESULTS

FTE calculations, based on an example workload of 100 adaptive and 200 nonadaptive patients per year, for three staffing scenarios were provided: medical physicists as planners and adaptors (2.9 FTE), medical physicists as planners but not adaptors (2.6 FTE), and medical physicists as adaptors but not planners (1.4 FTE). These findings offer calculation guidance and benchmarks for staffing requirements in CBCT-guided online adaptive radiotherapy programs, emphasizing the need for specific staffing models to accommodate the complexities of adaptive radiotherapy.

CONCLUSION

This study outlines a framework for calculating FTE requirements for medical physicists in a CBCT-guided online adaptive radiotherapy program. By analyzing the processes for three common adaptive radiotherapy workflows, this work can provide effective workforce planning and resource allocation estimates. This analysis can be used either prior to the implementation of an online adaptive radiotherapy program, for program development, or as a review of current practices to ensure operational efficiency and proper staffing levels are maintained.

Navigating the 2021 ACPSEM ROMP workforce model: insights from a single institution

Workforce modelling for Radiation Oncology Medical Physicists (ROMPs) is evolving and challenging, prompting the development of the 2021 Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) ROMP Workforce (ARW) Model. In the exploration of this model at Sir Charles Gairdner Hospital, a comprehensive productivity exercise was conducted to obtain a detailed breakdown of ROMP time at a granular level. The results provide valuable insights into ROMP activities and enabled an evaluation of ARW Model calculations. The findings also capture the changing ROMP role as evidenced by an increasing involvement in consultation and advisory tasks with other professionals in the field. They also suggest that CyberKnife QA time requirements in the data utilised by the model may need to be revised. This study emphasises features inherent in the model, that need to be understood if the model is to be applied correctly.

A national survey on the medical physics workload of external beam radiotherapy in Japan†

Several staffing models are used to determine the required medical physics staffing, including radiotherapy technologists, of radiation oncology departments. However, since Japanese facilities tend to be smaller in scale than foreign ones, those models might not apply to Japan. Therefore, in this study, we surveyed workloads in Japan to estimate the optimal medical physics staffing in external beam radiotherapy. A total of 837 facilities were surveyed to collect information regarding radiotherapy techniques and medical physics specialists (RTMPs). The survey covered facility information, staffing, patient volume, equipment volume, workload and quality assurance (QA) status. Full-time equivalent (FTE) factors were estimated from the workload and compared with several models. Responses were received from 579 facilities (69.2%). The median annual patient volume was 369 at designated cancer care hospitals (DCCHs) and 252 across all facilities. In addition, the median FTE of RTMPs was 4.6 at DCCHs and 3.0 at all sites, and the average QA implementation rate for radiotherapy equipment was 69.4%. Furthermore, advanced treatment technologies have increased workloads, particularly in computed tomography simulations and treatment planning tasks. Compared to published models, larger facilities (over 500 annual patients) had a shortage of medical physics staff. In very small facilities (about 140 annual patients), the medical physics staffing requirement was estimated to be 0.5 FTE, implying that employing a full-time medical physicist would be inefficient. However, ensuring the quality of radiotherapy is an important issue, given the limited number of RTMPs. Our study provides insights into optimizing staffing and resource allocation in radiotherapy departments.

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

PURPOSE

Cancer patient navigators (CPNs) can decrease the time from diagnosis to treatment, but workloads vary widely, which may lead to burnout and less optimal navigation. Current practice for patient distribution among CPNs at our institution approximates random distribution. A literature search did not uncover previous reports of an automated algorithm to distribute patients to CPNs. We sought to develop an automated algorithm to fairly distribute new patients among CPNs specializing in the same cancer type(s) and assess its performance through simulation on a retrospective data set.

METHODS

Using a 3-year data set, a proxy for CPN work was identified and multiple models were developed to predict the upcoming week's workload for each patient. An XGBoost-based predictor was retained on the basis of its superior performance. A distribution model was developed to fairly distribute new patients among CPNs within a specialty on the basis of predicted work needed. The predicted work included the week's predicted workload from a CPN's existing patients plus that of newly distributed patients to the CPN. Resulting workload unfairness was compared between predictor-informed and random distribution.

RESULTS

Predictor-informed distribution significantly outperformed random distribution for equalizing weekly workloads across CPNs within a specialty.

CONCLUSION

This derivation work demonstrates the feasibility of an automated model to distribute new patients more fairly than random assignment (with unfairness assessed using a workload proxy). Improved workload management may help reduce CPN burnout and improve navigation assistance for patients with cancer.

Report of the ACPSEM radiation oncology medical physics workforce modelling project task group

The ACPSEM radiation oncology medical physics workforce modelling project task group was formed to acquire a snapshot of practices in Australia and New Zealand and to develop an activity-based workforce model. To achieve this, two surveys were carried out, capturing the work practices of 98 radiation oncology departments and 182 college members. The member survey provided a snapshot of the current workforce: their demographics, work conditions, professional recognition, and future plans. The facility survey provided an Australian and New Zealand contextualisation of the volume-based activities defined in the International Atomic Energy Agency activity-based radiation oncology staffing model at a granular level. An ACPSEM ROMP workforce model was developed to be a modelling tool applicable at both the facility and sector levels.

A system for equitable workload distribution in clinical medical physics

Background

Clinical medical physics duties include routine tasks, special procedures, and development projects. It can be challenging to distribute the effort equitably across all team members, especially in large clinics or systems where physicists cover multiple sites. The purpose of this work is to study an equitable workload distribution system in radiotherapy physics that addresses the complex and dynamic nature of effort assignment.

Methods

We formed a working group that defined all relevant clinical tasks and estimated the total time spent per task. Estimates used data from the oncology information system, a survey of physicists, and group consensus. We introduced a quantitative workload unit, “equivalent workday” (eWD), as a common unit for effort. The sum of all eWD values adjusted for each physicist's clinical full‐time equivalent yields a “normalized total effort” (nTE) metric for each physicist, that is, the fraction of the total effort assigned to that physicist. We implemented this system in clinical operation. During a trial period of 9 months, we made adjustments to include tasks previously unaccounted for and refined the system. The workload distribution of eight physicists over 12 months was compared before and after implementation of the nTE system.

Results

Prior to implementation, differences in workload of up to 50% existed between individual physicists (nTE range of 10.0%–15.0%). During the trial period, additional categories were added to account for leave and clinical projects that had previously been assigned informally. In the 1‐year period after implementation, the individual workload differences were within 5% (nTE range of 12.3%–12.8%).

Conclusion

We developed a system to equitably distribute workload and demonstrated improvements in the equity of workload. A quantitative approach to workload distribution improves both transparency and accountability. While the system was motivated by the complexities within an academic medical center, it may be generally applicable for other clinics.

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

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.