ESTRO 2025 - Abstract Book

S2278

Interdisciplinary – Health economics & health services research

ESTRO 2025

interventions instead of one, increasing recourse demand. To manage this future scenario, we evaluated multiple approaches, including different scheduling configurations and resource capacities. The optimized model outcomes were implemented in August 2023. After one year, we assessed their effectiveness by comparing results with actual clinical data. Results: Figure 2 shows the method’s predictions versus actual outcomes. Using pre-transition input values (scenario 1), the model predicted a drop in treated patients from 172 (2022-2023) to 140. By increasing OR-time and adjusting scheduling configurations, patient numbers increased and OR-utilization stays constant (scenario 2). These insights prompted an increase in OR-time and a revised allocation strategy with morning and full-day shifts. The real-world results fell within the 95% CI (149-165) of the simulation when the actual input values were applied (scenario 3). Conclusion: This study supports our hypothesis, showing that the approach enables data-driven decision-making, with actual outcomes falling within the model’s 95% CI. This reliability highlights its potential for future application, providing a robust framework for resource allocation that mirrors real-world performance. While tailored to brachytherapy, it can also improve resource allocation and process efficiency in other complex oncology treatments.

Keywords: Process efficiency, Multidisciplinairy, Simulation