ESTRO 2025 - Abstract Book

S2277

Interdisciplinary – Health economics & health services research

ESTRO 2025

Non-metastatic breast and prostate cancers were excluded. Data were collected from patient medical records, and descriptive statistical analysis was performed. Pre- and post-TAPS clinic comparisons was made for wait times and patients' RTT on their first consultation. Results: During the first two year of the study (2022-2024), the TAPS clinic received 700 referrals. 200 eligible patients, resulted in 482 orders, referrals (63%), investigations (23%), and interventions (13%). Top three referrals were to a social worker (28%), dietitian (21%), and cancer patient navigator (10%). Commonly ordered investigations included CT scans (27%), MRIs (30%), and PET scans (21%). Median duration from referral to TAPS visits was 5 days, compared to 14 days for pre-TAPS patients seeking consultation with a RO. Post-TAPS, the mean wait time from RO consultation to treatment initiation significantly decreased from 22 to 14 days (p-value < 0.001). 77% of post-TAPS patients were ready for treatment at their first RO consultation, compared to only 53% of pre-TAPS patients (p-value < 0.001), resulting in a 25% reduction in RO return appointments for a management plan. Conclusion: The TAPS clinic implementation has shown the potential to impact the percentage of patients RTT positively. This data also sheds light on investigations required before RTT and the potential role of physician extenders within the health care team to facilitate accelerated workup of malignancies. While TAPS study data will help inform local cancer care program operations, it is also considered relevant to the broader RT community and professional RT societies addressing standards related to RT access. Digital Poster A novel framework for resource optimization demonstrated for brachytherapy: Integrating linear programming and discrete event simulation Lente Christan, Tezontl Rosario, Rob van Os, Albert Oostendorp, Bradley Pieters, Karel Hinnen, Danique Barten Department of Radiation Oncology, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands Purpose/Objective: Radiotherapy is a complex, multidisciplinary process with many interdependencies. While prior studies have focused on individual elements, comprehensive modeling that addresses the entire treatment chain — including operating room (OR) scheduling with pre- and post-appointments — remains underexplored. This study addresses these gaps by focusing on the brachytherapy department, specifically modeling its workflow complexities and optimizing resource allocation. We aim to develop a method that evaluates optimal resource distribution across the treatment chain, examining how variables like resource availability and process times affect key performance indicators (KPIs) such as patient throughput. We hypothesize that this approach will support data driven decision-making, producing results where actual outcomes fall within the model’s 95% confidence interval (CI). Material/Methods: We employed a combination of linear programming (LP) and discrete event simulation (DES). Figure 1 outlines the input parameters and KPIs used in the combined models. LP was used to optimize the resource allocation (i.e. OR time, staff, equipment). The DES model provided a detailed simulation of the multidisciplinary workflow, allowing for scenario-based analyses. The optimized output from the LP model served as input for the DES model, creating a sequential process for evaluation. In this study, the method is applied to model the transition from pulsed dose-rate to high dose-rate treatment (interstitial or combined intracavitary/interstitial applications). This requires patients to undergo two OR- Keywords: optimization, timely, radiotherapy 1385