AFOMP POLICY STATEMENT No. 2: recommended clinical radiation oncology medical physicist staffing levels in AFOMP countries

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.

AFOMP policy number 6: code of ethics for medical physicists in AFOMP Countries

This policy statement, which is the sixth of a series of documents prepared by the Asia-Oceania Federation of Organizations for Medical Physics (AFOMP) Professional Development Committee, gives guidance on how medical physicists in AFOMP countries should conduct themselves in an ethical manner in their professional practice (Ng et al. in Australas Phys Eng Sci Med 32:175-179, 2009; Round et al. in Australas Phys Eng Sci Med 33:7-10, 2010; Round et al. in Australas Phys Eng Sci Med 34:303-307, 2011; Round et al. in Australas Phys Eng Sci Med 35:393-398, 2012; Round et al. in Australas Phys Eng Sci Med 38:217-221, 2015). It was developed after the ethics policies and codes of conducts of several medical physics societies and other professional organisations were studied. The policy was adopted at the Annual General Meeting of AFOMP held in Jaipur, India, in November 2017.

Medical physics aspects of cancer care in the Asia Pacific region: 2011 survey results

Background:

Medical physicists are essential members of the radiation oncology team. Given the increasing complexity of radiotherapy delivery, it is important to ensure adequate training and staffing. The aim of the present study was to update a similar survey from 2008 and assess the situation of medical physicists in the large and diverse Asia Pacific region.

Methods:

Between March and July 2011, a survey on profession and practice of radiation oncology medical physicists (ROMPs) in the Asia Pacific region was performed. The survey was sent to senior physicists in 22 countries. Replies were received from countries that collectively represent more than half of the world’s population. The survey questions explored five areas: education, staffing, work patterns including research and teaching, resources available, and job satisfaction.

Results and discussion:

Compared to a data from a similar survey conducted three years ago, the number of medical physicists in participating countries increased by 29% on average. This increase is similar to the increase in the number of linear accelerators, showing that previously identified staff shortages have yet to be substantially addressed. This is also highlighted by the fact that most ROMPs are expected to work overtime often and without adequate compensation. While job satisfaction has stayed similar compared to the previous survey, expectations for education and training have increased somewhat. This is in line with a trend towards certification of ROMPs.

Conclusion:

As organisations such as the International Labour Organization (ILO) start to recognise medical physics as a profession, it is evident that despite some encouraging signs there is still a lot of work required towards establishing an adequately trained and resourced medical physics workforce in the Asia Pacific region.

Medical physics staffing for radiation oncology: a decade of experience in Ontario, Canada

The January 2010 articles in The New York Times generated intense focus on patient safety in radiation treatment, with physics staffing identified frequently as a critical factor for consistent quality assurance. The purpose of this work is to review our experience with medical physics staffing, and to propose a transparent and flexible staffing algorithm for general use. Guided by documented times required per routine procedure, we have developed a robust algorithm to estimate physics staffing needs according to center‐specific workload for medical physicists and associated support staff, in a manner we believe is adaptable to an evolving radiotherapy practice. We calculate requirements for each staffing type based on caseload, equipment inventory, quality assurance, educational programs, and administration. Average per‐case staffing ratios were also determined for larger‐scale human resource planning and used to model staffing needs for Ontario, Canada over the next 10 years. The workload specific algorithm was tested through a survey of Canadian cancer centers. For center‐specific human resource planning, we propose a grid of coefficients addressing specific workload factors for each staff group. For larger scale forecasting of human resource requirements, values of 260, 700, 300, 600, 1200, and 2000 treated cases per full‐time equivalent (FTE) were determined for medical physicists, physics assistants, dosimetrists, electronics technologists, mechanical technologists, and information technology specialists, respectively.

PACS numbers: 87.55.N‐, 87.55.Qr