Lindberg Jesper, Björk-Eriksson Thomas, Olsson Caroline E
Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden.
Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden.
Tech Innov Patient Support Radiat Oncol. 2021 Sep 30;20:10-16. doi: 10.1016/j.tipsro.2021.09.001. eCollection 2021 Dec.
Resources in radiotherapy (RT) need to be used effectively to meet the current clinical demand. The aim of this data-driven study is to identify temporal trends in the scheduling of patients for RT and to develop a tool for a visual overview of future scheduling levels.
Scheduling data at an eight-linac modern RT department in Sweden were collected twice daily for planned and observed linac use in 2018-2020. Information was retrieved each day for the present (Day 0) and the forthcoming 100 weekdays with total linac utilization rates (LURs) calculated for two activity categories: and . An in-house tool based on the LUR concept, database queries from the oncology information system (OIS)/automatic calculations was developed and evaluated by RT managers and scheduling staff (n = 10).
Overall median LURs were 87%/89% (planned/observed; p < 0.01) with more frequent and larger daily increase for non-treatment activities compared with treatment activities. LUR increased with shorter planning horizons and reached 100% for fully-operating linacs ≈3 weeks before Day 0. The tool was reported by 88% to ease the work and to contribute towards an even scheduling of patients (responses: 8/10).
Alterations from a planned RT schedule occurs frequently. Having a tool that helps to reduce the abundance of booking information into clinically relevant overviews promise to increase the understanding of present and future scheduling levels. Our proposed concept and tool suggest that this is a feasible approach to schedule patients for RT more evenly.
放射治疗（RT）资源需要有效利用，以满足当前临床需求。这项数据驱动研究的目的是确定RT患者排程的时间趋势，并开发一种工具，以直观呈现未来的排程水平。
收集瑞典一家拥有八台直线加速器的现代RT科室的排程数据，于2018 - 2020年期间每天两次记录计划和实际的直线加速器使用情况。每天获取当前（第0天）及未来100个工作日的信息，并针对两个活动类别计算直线加速器总利用率（LUR）：[此处原文缺失两个活动类别的具体内容]。基于LUR概念、肿瘤信息系统（OIS）的数据库查询/自动计算开发了一个内部工具，并由RT经理和排程人员（n = 10）进行评估。
总体LUR中位数为87%/89%（计划/实际；p < 0.01），与治疗活动相比，非治疗活动的每日增加更频繁且幅度更大。LUR随着计划时间缩短而增加，在第0天前约3周，全负荷运行的直线加速器LUR达到100%。88%的人报告称该工具简化了工作，并有助于实现患者排程的均衡（回复：8/10）。
RT计划排程经常发生变动。拥有一种有助于将大量预约信息简化为临床相关概述的工具，有望增进对当前和未来排程水平的理解。我们提出的概念和工具表明，这是更均衡地为RT患者排程的可行方法。
