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通过事件驱动的 FMEA 反馈回路提高放射治疗过程的安全性。

Improving the safety of radiotherapy treatment processes via incident-driven FMEA feedback loops.

机构信息

Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.

出版信息

J Appl Clin Med Phys. 2024 Sep;25(9):e14455. doi: 10.1002/acm2.14455. Epub 2024 Aug 5.

Abstract

BACKGROUND

Failure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates.

PURPOSE

A novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes.

METHODS

Twenty-three pre-identified failure modes of our external beam radiotherapy process, covering the process steps from patient admission to treatment delivery, were proffered on dedicated FMEA feedback and incident reporting terminals generated by the IRS. The clinical setting involved a computed tomography scanner, dosimetry, and five linacs. Incoming reports were used as basis to identify additional failure modes or confirm initial ones. The Kruskal-Wallis H test was applied to compare the risk priorities of the retrospective and prospective failure modes. Wald's sequential probability ratio test was used to investigate the correctness of the experts' occurrence ratings by means of the number of incoming reports.

RESULTS

Over a 15-month period, 304 reports were submitted. There were 0.005 (confidence interval [CI], 0.0014-0.0082) reported incidents per imaging study and 0.0006 (CI, 0.0003-0.0009) reported incidents per treatment fraction. Sixteen additional failure modes could be identified, and their risk priorities did not differ from those of the initial failure modes (p = 0.954). One failure mode occurrence rating could be increased, whereas the other 22 occurrence ratings could not be disproved.

CONCLUSIONS

Our approach is suitable for validating FMEAs and deducing additional failure modes on a continual basis. Accurate error rates can only be provided if a sufficient number of reports is available.

摘要

背景

失效模式与影响分析(FMEA)是放射治疗风险评估的一种有价值的工具,但由于未能识别失效模式或缺乏准确的错误率,其输出可能不可靠。

目的

测试并评估了一种与 FMEA 数据库相关联的新型事件报告系统(IRS)。该研究调查了该系统是否适合验证先前进行的分析,以及它是否能够提供准确的错误率,以支持先前确定的失效模式的专家发生评估。

方法

23 种预先确定的外照射放射治疗过程失效模式,涵盖从患者入院到治疗交付的过程步骤,在 IRS 生成的专用 FMEA 反馈和事件报告终端上提出。临床环境涉及 CT 扫描仪、剂量测定和 5 台直线加速器。新收到的报告被用作识别额外失效模式或确认初始失效模式的依据。Kruskal-Wallis H 检验用于比较回顾性和前瞻性失效模式的风险优先级。Wald 序贯概率比检验用于通过收到的报告数量来调查专家发生评估的正确性。

结果

在 15 个月的时间里,提交了 304 份报告。每例影像学研究的报告事件发生率为 0.005(置信区间[CI],0.0014-0.0082),每例治疗分次的报告事件发生率为 0.0006(CI,0.0003-0.0009)。可以识别出 16 种额外的失效模式,其风险优先级与初始失效模式没有差异(p=0.954)。一种失效模式的发生评估可以增加,而其他 22 种失效模式的发生评估不能被否定。

结论

我们的方法适用于验证 FMEA 并持续推导出额外的失效模式。只有在有足够数量的报告的情况下,才能提供准确的错误率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e87c/11492396/6819ab144247/ACM2-25-e14455-g005.jpg

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