Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Division of Radiation Oncology, Stellenbosch University and Tygerberg Hospital, Cape Town, 7505, South Africa.
Med Phys. 2019 Jun;46(6):2567-2574. doi: 10.1002/mp.13552. Epub 2019 May 6.
To assess the risk of failure of a recently developed automated treatment planning tool, the radiation planning assistant (RPA), and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA.
We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a four-field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic's standard QA processes and consisted of three components: (a) automatic, independent verification of the results of automated planning; (b) automatic comparison of treatment parameters to expected values; and (c) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program.
In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top-ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician.
Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified three key aspects of safe deployment of automated planning: (a) user training on potential failure modes; (b) comprehensive manual plan review by physicians and physicists; and (c) automated QA of the treatment plan.
评估最近开发的自动化治疗计划工具——放射计划助手（RPA）的失败风险，并确定实施专门为 RPA 设计的质量保证（QA）计划可降低多少风险。
我们使用失效模式与影响分析（FMEA）来评估 RPA 的风险。确定使用 RPA 规划宫颈癌四野箱照射治疗的工作流程中的各个步骤。然后，根据发生的可能性、严重程度和未被发现的可能性，识别并对每个步骤的潜在失效模式及其原因进行评分。此外，还评估了 QA 计划对失效模式可检测性的影响。QA 计划旨在补充诊所的标准 QA 流程，由三个部分组成：（a）自动、独立地验证自动化规划的结果；（b）自动比较治疗参数与预期值；（c）指导手动检查治疗计划。在使用和不使用 QA 计划的情况下，为每个潜在失效模式计算风险优先数（RPN）。
在 RPA 自动化治疗计划工作流程中，我们确定了 68 种潜在失效模式，共 113 个原因。在不使用 QA 计划时，平均 RPN 为 91，使用 QA 计划时为 68（最大 RPN 分别为 504 和 315）。RPN 的降低是由于提高了检测失效的可能性，从而降低了可检测性评分。排名最高的失效模式包括标记等中心点的错误识别、光束孔径定义不当、处方输入到 RPA 计划指令不正确以及医生未进行全面的计划审查。
我们使用 FMEA 评估了自动化治疗计划工作流程的临床部署风险，并表明 RPA 的专门 QA 计划（包括自动 QA 技术）提高了失效的可检测性，从而降低了风险。但是，仍存在一些残余风险，这些风险与手动治疗计划中的风险相似，人为错误仍然是潜在失效的主要原因。通过风险分析过程，我们确定了自动化规划安全部署的三个关键方面：（a）对潜在失效模式进行用户培训；（b）医生和物理学家对计划进行全面的手动审查；（c）对治疗计划进行自动化 QA。
