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.