A model for managing patient booking in a radiotherapy department with differentiated waiting times

Background. In Denmark, the waiting time from the ready-to-treat date to the first radiotherapy fraction is by national legislation guaranteed not to exceed 4 weeks. This guarantee has now been tightened for some specific diagnoses as it is required that e.g. intestinal and head and neck cancer patients have to be treated without unnecessary delays. Thus, patients with these tumour sites have to start radiotherapy treatment immediately after diagnosis, if it is their primary treatment modality. Previously, patients have been booked at the first empty time slot after their ready-to-treat date. Now, booking has to take the differentiated waiting times into account. To facilitate this, a model has been developed. It is used to manage the booking of patients, reserve accelerator capacity for patients with no waiting time and establish the waiting times for other patients. Methods. The patients are divided into categories according to their waiting time guarantee and for each category a maximum waiting time is defined. The required daily accelerator capacity and average new starts rate for each waiting time category has been determined from the actual patient case-mix in the department. To account for variations in treatment capacity, a prospective daily accelerator capacity is set. Based on the prospective capacity, preparation times, maximum waiting times, and new starts rates, a maximum booking curve (MBC) and a lower limit curve (LLC) are derived. They show the daily maximum and minimum limits, respectively, for booking at future dates. Results. The method is evaluated by a retrospective analysis of actual number of appointments booked compared to the MBC and LLC in situations of both excessive workload and ineffective use of capacity. Conclusion. The model represents a tool for effectively managing the capacity in a radiotherapy department with differentiated waiting times. It improves the transparency of the booking process and prospective waiting times can easily be derived on a daily basis.