Wang Yi-Fang, Price Michael J, Elliston Carl D, Munbodh Reshma, Spina Catherine S, Horowitz David P, Kachnic Lisa A
Department of Radiation Oncology, New York-Presbyterian Columbia University Irving Medical Center.
Adv Radiat Oncol. 2023 Nov 3;9(3):101399. doi: 10.1016/j.adro.2023.101399. eCollection 2024 Mar.
The emerging online adaptive radiation therapy (OART) treatment strategy based on cone beam computed tomography allows for real-time replanning according to a patient's current anatomy. However, implementing this procedure requires a new approach across the patient's care path and monitoring of the "black box" adaptation process. This study identifies high-risk failure modes (FMs) associated with AI-driven OART and proposes an interdisciplinary workflow to mitigate potential medical errors from highly automated processes, enhance treatment efficiency, and reduce the burden on clinicians.
An interdisciplinary working group was formed to identify safety concerns in each process step using failure mode and effects analysis (FMEA). Based on the FMEA results, the team designed standardized procedures and safety checklists to prevent errors and ensure successful task completion. The Risk Priority Numbers (RPNs) for the top twenty FMs were calculated before and after implementing the proposed workflow to evaluate its effectiveness. Three hundred seventy-four adaptive sessions across 5 treatment sites were performed, and each session was evaluated for treatment safety and FMEA assessment.
The OART workflow has 4 components, each with 4, 8, 13, and 4 sequentially executed tasks and safety checklists. Site-specific template preparation, which includes disease-specific physician directives and Intelligent Optimization Engine template testing, is one of the new procedures introduced. The interdisciplinary workflow significantly reduced the RPNs of the high-risk FMs, with an average decrease of 110 (maximum reduction of 305.5 and minimum reduction of 27.4).
This study underscores the importance of addressing high-risk FMs associated with AI-driven OART and emphasizes the significance of safety measures in its implementation. By proposing a structured interdisciplinary workflow and integrated checklists, the study provides valuable insights into ensuring the safe and efficient delivery of OART while facilitating its effective integration into clinical practice.
基于锥形束计算机断层扫描的新兴在线自适应放射治疗（OART）治疗策略允许根据患者当前的解剖结构进行实时重新规划。然而，实施此程序需要在患者的护理路径上采用新方法，并监测“黑匣子”适应过程。本研究识别与人工智能驱动的OART相关的高风险失效模式（FMs），并提出一种跨学科工作流程，以减轻高度自动化过程中潜在的医疗错误，提高治疗效率，并减轻临床医生的负担。
成立了一个跨学科工作组，使用失效模式和影响分析（FMEA）来识别每个过程步骤中的安全问题。基于FMEA结果，团队设计了标准化程序和安全检查表，以防止错误并确保任务成功完成。在实施建议的工作流程之前和之后，计算了前二十个FMs的风险优先数（RPNs），以评估其有效性。在5个治疗地点进行了374次自适应治疗，每次治疗都进行了治疗安全性评估和FMEA评估。
OART工作流程有4个组成部分，每个部分分别有4、8、13和4个顺序执行的任务和安全检查表。特定部位模板准备是引入的新程序之一，其中包括针对特定疾病的医生指令和智能优化引擎模板测试。跨学科工作流程显著降低了高风险FMs的RPNs，平均降低了110（最大降低305.5，最小降低27.4）。
本研究强调了解决与人工智能驱动的OART相关的高风险FMs的重要性，并强调了安全措施在其实施中的重要性。通过提出结构化的跨学科工作流程和综合检查表，该研究为确保OART的安全有效实施提供了有价值的见解，同时促进其有效融入临床实践。
