Operations research for resource planning and -use in radiotherapy: a literature review

Abstract

Background: The delivery of radiotherapy (RT) involves the use of rather expensive resources and multi-disciplinary staff. As the number of cancer patients receiving RT increases, timely delivery becomes increasingly difficult due to the complexities related to, among others, variable patient inflow, complex patient routing, and the joint planning of multiple resources. Operations research (OR) methods have been successfully applied to solve many logistics problems through the development of advanced analytical models for improved decision making. This paper presents the state of the art in the application of OR methods for logistics optimization in RT, at various managerial levels.

An analytical approach to aggregate patient inflows to a simulation model over the radiotherapy process

“…Finally, the underlying data for our proposed similarity measure can easily be extracted from existing oncological information systems and the measure in itself is straightforward to calculate. Although the purpose of our system dynamics model over the RT process is not to find an optimal solution to the problem at hand, it is interesting to note that none of the aforementioned OR models for RT by Vieira [ 7 ], Kapamara [ 9 ] and Proctor [ 10 ] discuss grouping strategies or similarity measures when quantifying patient volumes as either an alternative to or together with probability distributions as estimated from observed data.…”

“…OR methods in RT have historically mainly focused on resource planning and resource use for purposes such as optimizing staff allocation, scheduling of patients or understanding the RT process from a strategic perspective [ 9 ]. Work on OR models for RT done by Vieira et al used the referral pattern of patients to RT for one month and, based on this, they could assume a daily patient volume based on the Poisson distribution with a mean number of patient arrivals corresponding to observed rates of a particular weekday [ 7 ].…”

Radiotherapy treatment scheduling considering time window preferences

“…Not only has it been shown that delays in the start of treatment may induce greater psychological distress in patients subject to longer waiting times [5], but also that 80% of the patients prefer a short interval (2 weeks or less) between referral and first oncology consultation [6]. The problem of scheduling RT treatment sessions for large varieties of treatment care pathways and technical constraints has been tackled by several studies in the current literature [7]. Models exist for assigning patients' irradiation sessions to linacs and days [8,9], with some studies addressing not only the scheduling component but also the sequencing of patients throughout the day [10].…”

“…However, manual endeavours to produce such a schedule by (several) staff members are usually time consuming, prone to errors, and likely to find sub-optimal solutions regarding the fulfillment of patient preference requests. Previous studies have approached different variants of the RT treatment scheduling problem and several methods have been proposed to solve it [7]. Sauré et al [8] formulated the problem as a discounted infinite-horizon Markov decision process, showing that the percentage of treatments initiating treatment within 10 days can potentially increase from 73% to 96%.…”

“…Results showed a potential reduction of average waiting times for radical (35 to 21.48 days) and palliative (15 to 13.10 days) patients. Although efficient methods for scheduling RT sessions have been proposed, the literature in relation to optimizing the sequencing of patients throughout the day is rather scarce [7]. However, as discussed above, besides complying with timeliness requirements and technical constraints, RT centers are often faced with the problem of finding a schedule that maximizes patient preferences regarding the starting time of irradiation sessions.…”