Date of Graduation
Statler College of Engineering and Mineral Resources
Industrial and Managements Systems Engineering
Many appointment-based clinical systems experience long waiting times. Consequently, these systems experience higher rates of cancellation or no-show. This problem creates dissatisfaction among customers, as well as inefficiencies in healthcare systems, but more importantly, increases medical complications due to postponement of care. As an added complication, sometimes no-showing patients will reschedule appointments and the rate and reschedule discipline can have significant effects on overall patient satisfaction and system efficiency. In this study, a one server, multi-class queuing network model is proposed in which patients have a probability of no-showing as well as a rescheduling rate. No-show and rescheduling rates are computed based on the current backlog of the system. This model categorizes patients into different classes, based on number of comorbidities, with individual service times and arrival rates. In addition to considering the differences of various classes of patients, the model also decreases the under-utilization of resources by considering the no-show and rescheduling rate of customers. The purpose of the model is to determine the number of patients representing the panel size allocated to a specific physician, with recommendations for adding physicians to alleviate increasing backlogs based on increasing rates of comorbidity. In the second section of the study, the appointment system is simulated, and its results are compared with those generated by queuing theory. A preference model is then introduced which gives patients an option of choosing among all available appointments. The simulation results suggest that allowing patients to choose their favorite appointment time does not affect overall system utilization.
Kiani, Mahsa, "Modeling Clinic Utilization by Considering Panel size, Multicomorbidities and Patient Scheduling" (2016). Graduate Theses, Dissertations, and Problem Reports. 5971.