Abstract

Appointment scheduling in an outpatient oncology clinic is a daunting task due to the stochastic and dynamic nature of the appointment requests. Each patient has a different trajectory and varying requirements of appointment length time that differ from one another. It is not possible to predict the amount of time required nor the amount of patients that will be treated in a day. Due to the oncologist's prescribed regimen, there is almost no flexibility to choose an appointment date because of the strict resting period required between treatments to achieve the best curative outcome.

The purpose of this thesis is to demonstrate the benefit of using integer programming to model and to solve some of the challenges faced by the Segal Cancer Center of the Jewish General Hospital in Montreal, Quebec, when designing appointment schedules. We study two scheduling problems.

The chemotherapy outpatient scheduling problem determines the allocation of patient appointment to days and the determination of appointment start time on those days for a planning horizon of four weeks. The objectives are to maximize the adherence to protocol, maximize the proper assignment of primary nurses to patients and minimize the completion date of treatments. With this model, the clinic can schedule appointment requests as they arise.

When taking an integrated approach to solve the oncology clinic multi-stage scheduling problem, it is possible to coordinate the clinic's departments and determine the start time of each activity required by patients no matter their trajectory through the system. Due to the minimization of patient wait time and the completion time of their visit, there will be a better coordination within the clinic, reduction of staff idle time and a balance of resource utilization. Most importantly, it will ensure the completion of tasks within a single day, eliminating the current two-days scheduling policy of the Segal Cancer Center.

The findings of this thesis will facilitate decision making in healthcare scheduling, improve the service level of oncology clinics and serve as a workforce management tool.