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.
1. European Cancer Information System - ECIS. Estimated incidence by year - summary 2022 [Available from: https://ecis.jrc.ec.europa.eu/explorer.php?$0-4$1-AE28E$4-1,2$3-0$6-0,85$5-All$7-7$21-0$2-All$CLongtermChart3_1$X0_14-$CLongtermChart3_2$X1_14-.
2. World Health O. Global strategy on human resources for health: workforce 2030. 2016.
3. Franco P, Tesio V, Bertholet J, Gasnier A, Del Portillo EG, Spalek M, et al. Professional quality of life and burnout amongst radiation oncologists: the impact of alexithymia and empathy. Radiotherapy and Oncology. 2020;147:162-8.
4. Di Tella M, Tesio V, Bertholet J, Gasnier A, Del Portillo EG, Spalek M, et al. Professional quality of life and burnout among medical physicists working in radiation oncology: The role of alexithymia and empathy. Physics and imaging in radiation oncology. 2020;15:38-43.
5. Franco P, Tesio V, Bertholet J, Gasnier A, Del Portillo EG, Spalek M, et al. The role of alexithymia and empathy on radiation therapists’ professional quality of life. Technical innovations & patient support in radiation oncology. 2020;15:29-36.
6. Lichter KE, Anderson J, Sim AJ, Baniel CC, Thiel CL, Chuter R, et al. Transitioning to Environmentally Sustainable, Climate-Smart Radiation Oncology Care. International Journal of Radiation Oncology, Biology, Physics. 2022;113(5):915-24.
7. Vieira B, Hans EW, van Vliet-Vroegindeweij C, Van De Kamer J, Van Harten W. Operations research for resource planning and-use in radiotherapy: a literature review. BMC medical informatics and decision making. 2016;16(1):1-11.
8. Vieira B, Demirtas D, van de Kamer JB, Hans EW, Jongste W, van Harten W. Radiotherapy treatment scheduling: Implementing operations research into clinical practice. Plos one. 2021;16(2):e0247428.