A national survey on the medical physics workload of external beam radiotherapy in Japan†.

Journal of radiation research(2023)

引用 0|浏览2
暂无评分
摘要
Several staffing models are used to determine the required medical physics staffing, including radiotherapy technologists, of radiation oncology departments. However, since Japanese facilities tend to be smaller in scale than foreign ones, those models might not apply to Japan. Therefore, in this study, we surveyed workloads in Japan to estimate the optimal medical physics staffing in external beam radiotherapy. A total of 837 facilities were surveyed to collect information regarding radiotherapy techniques and medical physics specialists (RTMPs). The survey covered facility information, staffing, patient volume, equipment volume, workload and quality assurance (QA) status. Full-time equivalent (FTE) factors were estimated from the workload and compared with several models. Responses were received from 579 facilities (69.2%). The median annual patient volume was 369 at designated cancer care hospitals (DCCHs) and 252 across all facilities. In addition, the median FTE of RTMPs was 4.6 at DCCHs and 3.0 at all sites, and the average QA implementation rate for radiotherapy equipment was 69.4%. Furthermore, advanced treatment technologies have increased workloads, particularly in computed tomography simulations and treatment planning tasks. Compared to published models, larger facilities (over 500 annual patients) had a shortage of medical physics staff. In very small facilities (about 140 annual patients), the medical physics staffing requirement was estimated to be 0.5 FTE, implying that employing a full-time medical physicist would be inefficient. However, ensuring the quality of radiotherapy is an important issue, given the limited number of RTMPs. Our study provides insights into optimizing staffing and resource allocation in radiotherapy departments.
更多
查看译文
关键词
workload, medical physicist, radiotherapy technologist, radiation oncology, full-time equivalent, staffing model
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要