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Using national data to model the New Zealand radiation oncology workforce

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...

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Autores principales: Dunn, Alex, Costello, Shaun, Imlach, Fiona, Jo, Emmanuel, Gurney, Jason, Simpson, Rose, Sarfati, Diana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9542613/
https://www.ncbi.nlm.nih.gov/pubmed/35768935
http://dx.doi.org/10.1111/1754-9485.13448
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author Dunn, Alex
Costello, Shaun
Imlach, Fiona
Jo, Emmanuel
Gurney, Jason
Simpson, Rose
Sarfati, Diana
author_facet Dunn, Alex
Costello, Shaun
Imlach, Fiona
Jo, Emmanuel
Gurney, Jason
Simpson, Rose
Sarfati, Diana
author_sort Dunn, Alex
collection PubMed
description 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.
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spelling pubmed-95426132022-10-14 Using national data to model the New Zealand radiation oncology workforce Dunn, Alex Costello, Shaun Imlach, Fiona Jo, Emmanuel Gurney, Jason Simpson, Rose Sarfati, Diana J Med Imaging Radiat Oncol RADIATION ONCOLOGY 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. John Wiley and Sons Inc. 2022-06-29 2022-08 /pmc/articles/PMC9542613/ /pubmed/35768935 http://dx.doi.org/10.1111/1754-9485.13448 Text en © 2022 The Authors. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RADIATION ONCOLOGY
Dunn, Alex
Costello, Shaun
Imlach, Fiona
Jo, Emmanuel
Gurney, Jason
Simpson, Rose
Sarfati, Diana
Using national data to model the New Zealand radiation oncology workforce
title Using national data to model the New Zealand radiation oncology workforce
title_full Using national data to model the New Zealand radiation oncology workforce
title_fullStr Using national data to model the New Zealand radiation oncology workforce
title_full_unstemmed Using national data to model the New Zealand radiation oncology workforce
title_short Using national data to model the New Zealand radiation oncology workforce
title_sort using national data to model the new zealand radiation oncology workforce
topic RADIATION ONCOLOGY
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9542613/
https://www.ncbi.nlm.nih.gov/pubmed/35768935
http://dx.doi.org/10.1111/1754-9485.13448
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