Using national data to model the New Zealand radiation oncology workforce. Issue 5 (29th June 2022)

• Record Type: Journal Article
• Authors: Dunn, Alex; Costello, Shaun; Imlach, Fiona; Jo, Emmanuel; Gurney, Jason; Simpson, Rose; Sarfati, Diana
• Is Part Of: Journal of medical imaging and radiation oncology. Volume 66:Issue 5(2022)
• Page Start: 708
• Page End: 716
• Publication Date: 2022-06-29
• Subjects: Health Workforce -- Health Workforce Modelling -- Health Workforce Supply and Demand Planning -- radiation oncology -- radiotherapy/radiation therapy; Radiology, Medical -- Periodicals; Radiology, Medical -- Australasia -- Periodicals; 616.0757
• DOI: 10.1111/1754-9485.13448
• Languages: English
• ISSNs: 1754-9477

Abstract

Abstract: Introduction: Demand for radiation therapy is expected to increase over time. In Aotearoa/New Zealand, the radiation oncology workforce experiences high numbers of clinical hours but an intervention rate that is lower than in comparable countries, suggesting unmet treatment need. Accurate models on the supply and demand for radiation oncologists (ROs) are needed to ensure adequate staffing levels. Methods: We developed a demand model that predicted the future number of ROs required, using national data from the Radiation Oncology Collection (ROC) and a survey of ROs. Radiation therapy intervention and retreatment rates (IR/RTRs), and benign and non‐cancer conditions being treated, were derived from the ROC and applied to Census population projections. Survey data provided definitions of treatment by complexity, time spent in different activities and time available for work. Results were linked to radiation oncology workforce forecasts from a supply model developed by the Ministry of Health. Results: The demand model showed that 85 ROs would be needed in 2031, if current IR/RTRs were maintained, an increase from 68 in 2021. The supply model predicted a decrease in ROs over time, leaving a significant shortfall. Model parameters could be modified to assess the impact of workforce or practice changes; more ROs would be needed if average working hours reduced or IR/RTRs increased. Conclusion: Workforce models based on robust data collections are an important tool for workforce planning. The RO demand model presented here combines detailed information on treatment and work activities to provide credible estimates that can be used to inform actions on training, recruitment and retention.