Enhancing clinical safety in radiation oncology: A data-driven approach to risk management.

Lukas Sölkner, Dietmar Georg, Uwe Wolff, Andreas Renner, Joachim Widder, Gerd Heilemann
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Abstract

Purpose: To demonstrate a data-driven risk management (RM) strategy in radiation oncology using an in-house developed web-based incident reporting system. The system leverages real-time analytics to enhance clinical risk prioritization and management, improving patient safety and treatment efficiency.

Methods: We developed and implemented a web-based incident reporting system that allows any staff member to report incidents in real time, supporting anonymous submissions and capturing detailed incident data. The collected data are followed up in monthly meetings of a dedicated multidisciplinary RM team that decides on respective interventions. Over five years, incident data were analyzed to assess the effectiveness of safety barriers-pre-planning, physics, and pre-treatment checks-in capturing incidents before they impact patient care and safety. The analysis focused on incident frequencies and the workflow steps where errors originated versus where they were detected, highlighting deficiencies and guiding improvements. When specific issues increased, a Failure Mode and Effects Analysis (FMEA) was initiated to identify and prioritize failure modes and implement corrective actions, such as new safety barriers or refining existing safety measures.

Results: The web-based incident reporting system enhances responsive incident reporting and tailors RM strategies effectively. Data analysis reveals incident frequencies and detection points, identifying errors that bypass safety barriers and enabling targeted countermeasures. Despite safety barriers intercepting many incidents, critical gaps were identified. Since implementing data-driven RM in 2019, the average number of process steps between incident cause and detection could be halved. Resource analysis indicates increased allocation is needed; development required approximately 150 h, and RM averages 20% of a full-time equivalent position.

Conclusion: Implementing the web-based incident reporting system has advanced RM in radiation oncology, ensuring legal compliance and enhancing safety through real-time analytics. The system effectively identifies and mitigates risks, strengthening QA barriers as evidenced by decreased time between error origin and detection. Adequate resource allocation is essential to sustain these improvements. Increasing full-time equivalent allocations for RM activities is recommended.

加强放射肿瘤学的临床安全:数据驱动的风险管理方法。
目的:利用内部开发的基于网络的事件报告系统演示数据驱动的放射肿瘤学风险管理(RM)策略。该系统利用实时分析来加强临床风险的优先排序和管理,提高患者安全和治疗效率。方法:我们开发并实施了一个基于网络的事件报告系统,允许任何工作人员实时报告事件,支持匿名提交和捕获详细的事件数据。收集的数据在一个专门的多学科RM团队的每月会议上进行跟踪,决定各自的干预措施。在五年多的时间里,对事故数据进行了分析,以评估安全屏障(预先计划、物理和治疗前检查)的有效性,并在事故影响患者护理和安全之前捕获事故。分析的重点是事件频率和错误产生的工作流程步骤,以及错误被检测到的地方,突出缺陷并指导改进。当具体问题增加时,启动故障模式和影响分析(FMEA)来识别和优先考虑故障模式并实施纠正措施,例如新的安全屏障或改进现有的安全措施。结果:基于web的事件报告系统增强了响应性事件报告,并有效地定制了RM策略。数据分析揭示了事故频率和检测点,识别绕过安全屏障的错误,并启用有针对性的对策。尽管安全屏障拦截了许多事故,但仍发现了关键漏洞。自2019年实施数据驱动的RM以来,事故原因和检测之间的平均流程步骤数量可以减少一半。资源分析表明需要增加拨款;开发大约需要150 h, RM平均为全职同等职位的20%。结论:实施基于网络的事件报告系统,促进了放射肿瘤学RM的发展,确保了法律合规,并通过实时分析提高了安全性。该系统有效地识别和减轻了风险,通过减少错误起源和检测之间的时间来加强QA障碍。充足的资源分配对于维持这些改进至关重要。建议增加管理活动的全时等值拨款。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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