Rapid Evolution of Radiotherapy Technology Presents an Ever-Changing Landscape for Medical Physics Teams

Posted: July 21, 2022

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(Left) One of four linear accelerators used for radiotherapy located in the Simcoe Muskoka Regional Cancer Program at RVH; (Right) Kyle Malkoske, Medical Physicist and Head of Medical Physics at RVH

BARRIE, ON - Radiotherapy is a rapidly evolving, technology-driven field that uses radiation to destroy or damage cancer cells without hurting nearby healthy cells. A hospital’s medical physics department is primarily responsible for the development and use of radiotherapy systems, infrastructure, and technical procedures. These teams are typically comprised of healthcare professionals who specialize in the medical practice of physics, including medical physicists, physics assistants (or associates), engineers, and information technology specialists.
 
Medical physics departments continuously navigate novel treatments and technologies to provide safe, accurate, and reliable care for patients. With responsibilities towards clinical service and consultation, research and development, and teaching, it is critical for these departments to periodically review their staffing resources, particularly to meet resource needs for increased patient volumes and to provide equipment support for newer technologies, such as advanced, high-precision radiotherapy.

In 2012, a team led by Ontario medical physicist Dr. Jerry Battista, Professor of Oncology and Medical Biophysics at The University of Western Ontario, developed a comprehensive, grid-based staffing algorithm to replace simpler, but less accurate models based primarily on caseload. This new model has since been well-validated across Canadian cancer centres, but, a growing discrepancy was observed in 2018 between the algorithm’s predictions and actual staffing levels in Ontario.

In 2018, Kyle Malkoske, Medical Physicist and Head of Medical Physics at RVH, joined forces with Dr. Battista and additional medical physicist colleagues Dr. Katharina Sixel and Dr. Robert Hunter to update the parameters of the 2012 algorithm.

As Kyle describes, “We strove to present a model that emphasized safety and innovation, but was also practical and cost-effective for healthcare systems.  This project provided a unique opportunity to liaise with colleagues from across the country and gain a deeper understanding of how different medical physics departments are operated.”

The team collected information on various medical physics activities from 15 radiation treatment centers in Ontario from April 1, 2018, to March 31, 2019, and drawing on the data collected, the 2012 algorithm was reviewed and adjusted through regression analysis to better align with observed clinical practices.

The updated algorithm was reorganized into five major components, including Clinical Procedures, Clinical Equipment, Core Services, Education & Training, and Administration.  It assigns weighted parameters for staffing, and accounts for variations in local needs and workload. In practice, users of the new algorithm can input their equipment inventory, patient treatment volumes, and other information to which the algorithm estimates the number of medical physics staff required. Indeed, the new algorithm was tested at 23 centres across Canada and yielded better agreement with actual staffing in Ontario.

The authors further compared the new algorithm with four widely referenced staffing models from outside Canada, including the 2014 European Commission, the 2015 International Atomic Energy Agency, and the 2017 Institute of Physics and Engineering in Medicine models. Overall, the new Ontario model showed the lowest staffing counts and the best agreement with observed staffing levels for three representative centres in Ontario, thus re-affirmed the use of the updated algorithm across the province.

The team published the updated model in the August 2021 edition of the Journal of Applied Clinical Medical Physics.

Since the publication, the algorithm has been used to improve staffing levels for an established centre in Ontario. Kyle has also received requests for the algorithm tool from hospitals in other provinces, as well as Europe, Asia, and the United States.