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基于不良事件学习系统数据的电子近距离放射治疗失效模式和效果分析的基准测试。

Benchmarking failure mode and effects analysis of electronic brachytherapy with data from incident learning systems.

机构信息

Department of Radiation Medicine & Applied Sciences, UC San Diego, La Jolla, CA.

Department of Radiation Medicine & Applied Sciences, UC San Diego, La Jolla, CA.

出版信息

Brachytherapy. 2021 May-Jun;20(3):645-654. doi: 10.1016/j.brachy.2020.11.014. Epub 2021 Jan 19.

Abstract

PURPOSE

Failure modes and effects analysis (FMEA) is a prospective risk assessment tool for identifying failure modes in equipment or processes and informing the design of quality control systems. This work aims to benchmark the performance of FMEAs for electronic brachytherapy (eBT) of the skin and for breast by comparing predicted versus actual failure modes reported in multiple incident learning systems (ILS).

METHODS AND MATERIALS

Two public and our institution's internal ILS were queried for Xoft Axxent eBT-related events over 9 years. The failure modes and Risk Priority Numbers (RPNs) were taken from FMEAs previously performed for Xoft eBT of nonmelanoma skin cancer and breast intraoperative radiation therapy (IORT). For each event, the treatment site and primary failure mode was compared with the failure modes and RPNs from that site's FMEA.

RESULTS

49 events involving Xoft eBT were identified. Thirty-one (63.3%) involved breast IORT, and 18 (36.7%) involved the skin. Three events could not be linked to an FMEA failure mode. In 87.7% of events, the primary failure mode ranked in the FMEA top 10 by RPNs. In 83.3% of skin events, the failure modes ranked in the top 10 by RPN or severity. In 90.3% of IORT events, the failure modes ranked within the top 10 by RPN or severity.

CONCLUSIONS

Evaluating FMEA failure modes against ILS data demonstrates that FMEA is effective at predicting failure modes but can be dependent on user experience. ILS data can improve FMEA by identifying potential failure modes and suggesting realistic occurrence, detectability, and severity values.

摘要

目的

失效模式与影响分析(FMEA)是一种前瞻性风险评估工具,用于识别设备或过程中的失效模式,并为质量控制系统的设计提供信息。本研究旨在通过比较多个事件学习系统(ILS)中报告的预测与实际失效模式,来对标电子近距离放疗(eBT)皮肤和乳房 FMEA 的性能。

方法与材料

对 9 年来 Xoft Axxent eBT 相关事件进行了两个公共和我们机构内部的 ILS 查询。失效模式和风险优先数(RPN)取自之前为 Xoft 非黑色素瘤皮肤癌和乳房术中放疗(IORT)eBT 进行的 FMEA。对于每个事件,将治疗部位和主要失效模式与该部位 FMEA 的失效模式和 RPN 进行比较。

结果

共确定了 49 例涉及 Xoft eBT 的事件。31 例(63.3%)涉及乳房 IORT,18 例(36.7%)涉及皮肤。有 3 个事件无法与 FMEA 失效模式相关联。在 87.7%的事件中,主要失效模式在 RPN 排名中位列 FMEA 前 10 位。在 83.3%的皮肤事件中,失效模式在 RPN 或严重程度上排名前 10 位。在 90.3%的 IORT 事件中,失效模式在 RPN 或严重程度上排名前 10 位。

结论

通过将 FMEA 失效模式与 ILS 数据进行评估,证明了 FMEA 可以有效地预测失效模式,但可能取决于用户的经验。ILS 数据可以通过识别潜在失效模式并提供实际发生、可检测性和严重程度值来改进 FMEA。

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