Vienna, Austria

ESTRO 2023

Session Item

Monday
May 15
08:45 - 10:00
Strauss 2
Our resources are limited! An insight on optimising radiotherapy resources for the future
Danique Barten, The Netherlands;
Vania Batista, Germany
Symposium
Young
09:03 - 09:21
1+1=3, no delays, everything is on time!
Nienke Hoffmans-Holtzer, The Netherlands
SP-0689

Abstract

1+1=3, no delays, everything is on time!
Authors:

Nienke Hoffmans-Holtzer1

1Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Radiotherapy, Rotterdam, The Netherlands

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Abstract Text

Increasing cancer incidence (1), staff shortage (2) and the high incidence of burnout among radiation oncologists (RO), physicists and radiation technologists (RTT) (3-5) is putting many departments under strain while increasing healthcare costs and awareness of the environmental effects of healthcare are imposing additional restrictions on healthcare systems (6). To overcome a part of these problems, the field of optimization research (OR) could provide valuable tools to optimize radiotherapy (RT) processes (7).

To date, however, only Vieira et al (8), reported on clinical implementation of an OR method in RT, which suggests a discrepancy between (mathematical) theory and clinical practice. Part of this discrepancy could be caused by the patient-to-patient variation in execution times in clinical practice and the lack of robustness against this uncertainty of optimized models.

In the setting of a dedicated one-stop shop (OSS) at the outpatient clinic for palliative RT at our institute, pretreatment preparation takes the majority of staff time. While all tasks are predefined and executed by dedicated staff (RO, RTT) or automated, generally, no tasks are scheduled with the exception of patient consult and CT acquisition. With the aim to optimize staff deployment, reduce patients’ waiting time and unnecessary delays at the OSS, a new robust scheduling method was developed which could cope with the stochastic nature of our OSS. This method is based on a non-dominated sorting genetic algorithm and two mixed integer linear programs, that could minimize the expected average patient preparation time (Fmean) and balance Fmean with the risk of delay for patients (i.e. overtime for staff) (RoO). Our scheduling approach was investigated in (i) different theoretical settings (1/2/3 RO, 2 RTT, 3/4/5 patients) and (ii) clinical practice (1 RO, 1 RTT, 3 patients).

The experiments provided insights in the trade-off between expected Fmean, RoO, working shift length, number of patients treated on a single day and staff composition. Therefore, this approach is a valuable tool for tactical and strategic decision making and the results strongly support further exploration of scheduling optimization for RT preparation, also outside an OSS-setting. While “everything is on time” is an utopia, with robust scheduling, delays can be mitigated.



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