Introduction and implementation of the basic treatment equivalent in a Varian-based department

Integer programming for improving radiotherapy treatment efficiency

Background and purpose

Patients received by radiotherapy departments are diverse and may be diagnosed with different cancers. Therefore, they need different radiotherapy treatment plans and thus have different needs for medical resources. This research aims to explore the best method of scheduling the admission of patients receiving radiotherapy so as to reduce patient loss and maximize the usage efficiency of service resources.

Materials and methods

A mix integer programming (MIP) model integrated with special features of radiotherapy is constructed. The data used here is based on the historical data collected and we propose an exact method to solve the MIP model.

Results

Compared with the traditional First Come First Served (FCFS) method, the new method has boosted patient admission as well as the usage of linear accelerators (LINAC) and beds.

Conclusions

The integer programming model can be used to describe the complex problem of scheduling radio-receiving patients, to identify the bottleneck resources that hinder patient admission, and to obtain the optimal LINAC-bed radio under the current data conditions. Different management strategies can be implemented by adjusting the settings of the MIP model. The computational results can serve as a reference for the policy-makers in decision making.

A Method of Predicting Workload and Staffing Level for Radiotherapy Treatment Planning as Plan Complexity Changes

When assessing the impact on workload from an expanding number of new patients and increasing treatment complexity, radiotherapy clinics find that oversimplified workload parameters, such as number of patients or number of fields, are not suitable for managing and predicting workload and organisational dimension. Although the basic treatment equivalent concept is available for predicting linear accelerator utilisation, no corresponding parameter has been available for radiotherapy treatment planning. In this study, we derive a simple workload indicator for treatment planning. The dose plan unit (DPU) takes the complexity of the treatment plan into account. Categorising plans according to complexity in their production, and measuring corresponding time for completing the plans in these categories, leads to the following baseline values for workload prediction: 1 DPU (non-computed tomography [CT]), 3 DPU (CT-contour) and 6 DPU (full-CT). The measured average productivity of 0.65 DPU per hour (1 standard deviation, SD = 0.08 DPU), or alternatively 1 DPU = 92 min, for a dosimetrist in this clinic indicates that 79.0 DPU can be produced by each dosimetrist per month within normal working hours. The predictive power of the DPU is shown in terms of using it to quantify the impact on workload in treatment planning of changing treatment protocols for a particular anatomic treatment site.