Solving operational problems in outpatient chemotherapy clinics using mathematical programming and simulation

Increasing number of cancer survivors besides effective medications increase the demand for cancer care services. Therefore, managers have to investigate new ways to enhance the operational performance of the outpatient chemotherapy clinics (OCCs). However, the management process is complex due to significant variability in treatment times as a result of the different cancer types and accordingly different chemotherapy protocols and scarce resources such as nurses, chemotherapy chairs/beds, and pharmacists. In this paper, we address two problems in OCCs. First, in the planning problem, the objective is assigning the optimum first day to start the treatment for a set of new patients, and computing the required number of nurses and pharmacists given the limited resources. Second, the operational problem of scheduling the patients’ appointments. In this problem, the objective is to set the best appointment schedules for all patients, new and existing to improve the operational performance of the clinic. As the two problems are highly interrelated, we propose a two-phase solution approach starting by a mixed integer programming model that assigns the starting day of treatment for new patients and finds the optimum number of needed nurses and pharmacists to fulfill two objectives. Then, in the second phase, a discrete event simulation model is used to generate patient appointment schedules that minimise the treatment delay for patients and the total completion times of treatments in each day under resources availability constraints, including two new constraints covering the drug availability and pharmacists working-hours. Finally, the proposed simulation model is applied for evaluating the operations performance of a current case study and finding the best scheduling rule for patient appointment times to achieve a minimum wait time in the OCC.