Sölkner Lukas, Georg Dietmar, Wolff Uwe, Renner Andreas, Widder Joachim, Heilemann Gerd
Department of Radiation Oncology, Medical University of Vienna/University Hospital Vienna, Vienna, Austria.
Department of Radiation Oncology, Medical University of Vienna/University Hospital Vienna, Vienna, Austria.
Z Med Phys. 2025 Mar 8. doi: 10.1016/j.zemedi.2025.02.003.
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.
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.
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.
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.
利用内部开发的基于网络的事件报告系统，展示放射肿瘤学中一种数据驱动的风险管理（RM）策略。该系统利用实时分析来加强临床风险的优先级划分和管理，提高患者安全和治疗效率。
我们开发并实施了一个基于网络的事件报告系统，该系统允许任何工作人员实时报告事件，支持匿名提交并收集详细的事件数据。收集到的数据在一个专门的多学科RM团队的月度会议上进行跟进，该团队决定相应的干预措施。在五年时间里，对事件数据进行分析，以评估安全屏障——预规划、物理和治疗前检查——在影响患者护理和安全之前捕获事件的有效性。分析重点是事件频率以及错误发生的工作流程步骤与检测到错误的步骤，突出缺陷并指导改进。当特定问题增加时，启动失效模式与效应分析（FMEA）以识别失效模式并确定其优先级，并实施纠正措施，如新的安全屏障或完善现有的安全措施。
基于网络的事件报告系统增强了事件报告的响应能力，并有效地定制了RM策略。数据分析揭示了事件频率和检测点，识别出绕过安全屏障的错误并促成有针对性的应对措施。尽管安全屏障拦截了许多事件，但仍发现了关键差距。自2019年实施数据驱动的RM以来，事件起因与检测之间的平均流程步骤数量可减半。资源分析表明需要增加资源分配；开发大约需要150小时，RM平均占一个全职等效岗位的20%。
实施基于网络的事件报告系统推动了放射肿瘤学中的RM，确保了法律合规性，并通过实时分析提高了安全性。该系统有效地识别和减轻风险，加强了质量保证屏障，错误起源与检测之间的时间减少证明了这一点。充足的资源分配对于维持这些改进至关重要。建议增加RM活动的全职等效分配。
