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首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >Managing a national radiation oncologist workforce: A workforce planning model
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Managing a national radiation oncologist workforce: A workforce planning model

机译：管理国家放射肿瘤科医生的劳动力：劳动力规划模型

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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.
机译：目的：在许多国家，放射肿瘤学专业在劳动力规划方面都遇到了重大挑战。我们的目的是开发和验证一个劳动力规划模型，该模型将在至少10年的时间范围内预测加拿大放射肿瘤学家的供求之间的平衡，从而确定对平衡产生最大影响的模型参数，以及建议该模型如何适用于其他国家。方法：创建一个前向计算模型，并使用从国家来源获得的数据进行填充。使用历史前瞻性方法确认了验证。结果：在基线假设下，该模型预测RO学员的短期过剩，然后是2020年的预计赤字。敏感性分析显示，接受放射治疗的机会（涉及到的病例数），个人RO的工作量，平均退休年龄和居民培训摄入量对供求平衡的影响最大。在这些参数的合理范围内，可能会出现大量的毕业生短缺或过剩的情况，这表明需要进行持续的监控。结论：可以使用基于当前系统特征和影响投影的可修改参数的投影计算模型来进行放射肿瘤学中的劳动力规划。工作量预测应为决策制定提供依据，以满足满足肿瘤健康服务需求所需的专业和培训计划居民人数的增长。所使用的方法适用于其他国家的放射肿瘤学和其他类似医学专业的劳动力规划。

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