University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA.
Department of Radiation Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
J Appl Clin Med Phys. 2022 Sep;23(9):e13694. doi: 10.1002/acm2.13694. Epub 2022 Jun 30.
To develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans.
A custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence-generated resident treatment plans from the RPA for safety and plan quality, five of which contained errors. Physicists performed plan checks, recorded errors, and rated each plan's clinical acceptability. Following a 2-week break, physicists reviewed 10 additional plans with a similar distribution of errors using our customized checklist. Participants then provided feedback on the usability of the checklist and it was modified accordingly. In a second study, this process was repeated with 14 senior medical physics residents who were randomly assigned to checklist or no checklist for their reviews. Each reviewed 10 plans, five of which contained errors, and completed the corresponding survey.
In the first study, the checklist significantly improved the rate of error detection from 3.4 ± 1.1 to 4.4 ± 0.74 errors per participant without and with the checklist, respectively (p = 0.02). Error detection increased by 20% when the custom checklist was utilized. In the second study, 2.9 ± 0.84 and 3.5 ± 0.84 errors per participant were detected without and with the revised checklist, respectively (p = 0.08). Despite the lack of statistical significance for this cohort, error detection increased by 18% when the checklist was utilized.
Our results indicate that the use of a customized checklist when reviewing automated treatment plans will result in improved patient safety.
开发一个清单,以提高在自动生成的放疗计划审查过程中错误检测的率。
使用美国医学物理学家协会任务组 275 和 315 的指导以及放射规划助手(RPA)失效模式和影响分析的结果,开发了一个定制的清单。RPA 是一个自动勾画和治疗计划工具。初步清单为每个自动生成的计划包含 90 个审查项目。在第一项研究中,从我们机构招募了 8 名熟悉 RPA 的物理学家。每位物理学家审查了 10 个来自 RPA 的人工智能生成的住院医师治疗计划,其中 5 个包含错误。物理学家进行了计划检查,记录了错误,并对每个计划的临床可接受性进行了评分。经过两周的休息后,物理学家使用我们定制的清单审查了另外 10 个具有类似错误分布的计划。参与者随后提供了对清单可用性的反馈,并相应地进行了修改。在第二项研究中,14 名高级医学物理住院医师随机分配使用清单或不使用清单进行审查,重复了这一过程。每位医生审查了 10 个计划,其中 5 个包含错误,并完成了相应的调查。
在第一项研究中,使用清单后,错误检测率从无清单时的 3.4 ± 1.1 提高到 4.4 ± 0.74,分别为每位参与者(p = 0.02)。使用定制清单时,错误检测率提高了 20%。在第二项研究中,无清单和有清单时分别检测到 2.9 ± 0.84 和 3.5 ± 0.84 个错误(p = 0.08)。尽管这一组没有统计学意义,但使用清单时错误检测率提高了 18%。
我们的结果表明,在审查自动化治疗计划时使用定制清单将提高患者安全性。