ESTRO 2021 Abstract Book

S1692

ESTRO 2021

Results A historical review of independent card checks conducted by the radiation therapists (RTs) between July 2020 to February 2021 for 503 treatment cases revealed that an average (SD) 9.3 (+/-6.9) clicks was registered by each user per card check across all clinical sites. Reviews on OIS are now limited to verification of patient schedule, charge code management, clinical notes and eventual completion of the card check. This API enables consistent and accurate review of the treated parameters of each treatment field (i.e. delivered monitor units, couch position) which was previously difficult to achieve with (Figure 1) high dependency on human factor. Conclusion The RT plan check API is a complementary QA tool that serves as a value-driven platform to assist the RTs in reviewing large amount of multifaceted treatment field parameters (including the agreement of applied setup corrections with the corresponding action-levels of imaging protocols) and identifies discrepancies in an efficient and consistent manner. PO-1993 Decision support for radiotherapy resource planning around vacation periods using a simulation model J. Lindberg 1,2,3 , M. Gurjar 1 , P. Holmström 1 , S. Hallberg 3 , T. Björk-Eriksson 3,4 , C. Olsson 1,3 1 University of Gothenburg, Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden; 2 Sahlgrenska University Hospital, Department of Medical Physics and Biomedical Engineering, Gothenburg, Sweden; 3 Western Sweden Healthcare Region, Regional Cancer Centre West, Gothenburg, Sweden; 4 University of Gothenburg, Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden Purpose or Objective In Sweden, workers have a legislated right to four weeks of vacation during June to August. For radiotherapy (RT), capacity is typically reduced eight weeks during this period, splitting the vacation period in two. Changes in capacity for a multistep process such as RT often result in both overtime and having unused resources at different periods. For this purpose, we developed a simulation model to find reasonable and effective strategies to help managers with resource planning. Materials and Methods The simulation model covered the preparatory part of the RT workflow (imaging, contouring and treatment planning) and the treatment part (linear accelerator [linac] capacity). It was based on patient referral/treatment fraction data from 2015-2016 (scaled to present level) of a large modern RT department in Sweden consisting of 11 linacs. Normal capacities were set at a level to not build persistent queues and the first fraction was assumed to require doubled linac use compared to the following fractions. Different levels of resource reductions and timing of these with the two workflow parts were evaluated. Results Normal capacity for the preparation/treatment part was set to 100/340 patients per week and fractions per day, respectively. Simultaneous capacity reduction of 35% (reduced ordinary staff with addition of temporary staff) for both parts during 8 weeks, resulted in a buildup of patients waiting to start treatment. This was followed by a high utilization of linacs when the vacation period ended (Figure 1a). When preparation capacity was lowered further, to 50%, patients waiting to start treatment was reduced but it took >6 weeks before linacs were fully utilized again (Figure 1b). Moving the vacation period for the preparation part two weeks earlier (35% reduction), there were fewer patients waiting to start treatment compared to the first two scenarios but still enough to utilize the linacs to full capacity after the vacation period (Figure 2a). Having the vacation period for the preparatory part start four weeks earlier than the treatment part resulted in a similar situation after the vacation period as for the previous scenario but with unused linac capacity before the beginning of the vacation period (Figure 2b).