Radiotherapy & Oncology
Volume 103, Issue 1 , Pages 123-129, April 2012

Managing a national radiation oncologist workforce: A workforce planning model

  • Teri Stuckless

      Affiliations

    • Division of Radiation Oncology, Cancer Care Program, NL, Canada
  • ,
  • Michael Milosevic

      Affiliations

    • Princess Margaret Hospital and Department of Radiation Oncology, University of Toronto, Canada
  • ,
  • Catherine de Metz

      Affiliations

    • Department of Radiation Oncology, Cancer Center of Southeastern Ontario, Kingston, Canada
  • ,
  • Matthew Parliament

      Affiliations

    • Radiation Oncology, University of Alberta, Canada
  • ,
  • Brent Tompkins

      Affiliations

    • Division of Radiation Oncology, Cancer Care Program, NL, Canada
  • ,
  • Michael Brundage

      Affiliations

    • Department of Radiation Oncology, Cancer Center of Southeastern Ontario, Kingston, Canada
    • Queen’s Cancer Research Institute, Kingston, Canada
    • Corresponding Author InformationCorresponding author. Address: Cancer Care and Epidemiology, Queen’s Cancer Research Institute, 10 Stuart Street, Level 2, Kingston, Ontario, Canada K7L 3N6.

Received 17 May 2011; received in revised form 28 October 2011; accepted 23 December 2011. published online 02 February 2012.

Abstract 

Purpose

The specialty of radiation oncology has experienced significant workforce planning challenges in many countries. Our purpose was to develop and validate a workforce-planning model that would forecast the balance between supply of, and demand for, radiation oncologists in Canada over a minimum 10-year time frame, to identify the model parameters that most influenced this balance, and to suggest how this model may be applicable to other countries.

Methods

A forward calculation model was created and populated with data obtained from national sources. Validation was confirmed using a historical prospective approach.

Results

Under baseline assumptions, the model predicts a short-term surplus of RO trainees followed by a projected deficit in 2020. Sensitivity analyses showed that access to radiotherapy (proportion of incident cases referred), individual RO workload, average age of retirement and resident training intake most influenced balance of supply and demand. Within plausible ranges of these parameters, substantial shortages or excess of graduates is possible, underscoring the need for ongoing monitoring.

Conclusions

Workforce planning in radiation oncology is possible using a projection calculation model based on current system characteristics and modifiable parameters that influence projections. The workload projections should inform policy decision making regarding growth of the specialty and training program resident intake required to meet oncology health services needs. The methods used are applicable to workforce planning for radiation oncology in other countries and for other comparable medical specialties.

Keywords: Radiation oncology, Human resources

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PII: S0167-8140(11)00765-1

doi:10.1016/j.radonc.2011.12.025

Radiotherapy & Oncology
Volume 103, Issue 1 , Pages 123-129, April 2012