Examining The Influence Of Dependent Demand Arrivals On Patient Scheduling

Abstract

This research examines the influence of batch appointments on patient scheduling systems. Batch appointments are characterized by multiple patients within a family desiring appointments within the same time frame. These patients are considered to be dependent amongst each other within the batch request for both arrival and no-shows. Three models are proposed to further understand the impact of these dependent demand arrivals. First, a multivariate statistical model is developed to understand the behavior of patients at public and private dental clinics. Results indicate that approximately 42% of all appointments are associated with a batch request. Also, there is a dependency among patients that are scheduled within the batch. Next, a stationary infinite-horizon Markov decision process is presented to determine the acceptance of batch appointment requests given that a finite number of open appointment slots have been reserved for same-day requests. Results indicate that the clinic should reject the request for a batch appointment when the expected number of patients in the system exceeds the number of available dentist and the probability of no-show is less than or equal to 0.10. In the final model, a finite-horizon stochastic dynamic programming model is constructed to understand the impact of the appointment demand types (i.e. individual versus batch) and overbooking on the total expected profit and the total number of patients that are overbooked. As a result, the scheduling coordinator should consider accepting batch appointments as overbooked rather than prescheduled patients