Dynamic Distributed Ambulatory Care Scheduling

We investigate an ambulatory care scheduling problem derived from a real case in Ontario, Canada that offers multi-appointment, multi-class, multi-priority treatments in geographically distributed campuses with multiple resources. We consider a dynamic setting with uncertain patient arrival and use of the emergency department. This problem is formulated as an infinite-horizon Markov decision process model. Since we cannot solve large-sized instances via conventional approaches, we hybridize this model with a neural network to simplify feasibility constraints while respecting all assumptions. Given the curse of dimensionality, we use an affine approximation architecture to estimate the value function. An equivalent linear programing model is solved through column generation in order to compute approximate optimal policies and derive two easy-to-implement scheduling policies. Simulation results demonstrate that the approximate optimal policy and heuristics outperform alternative scheduling policies. Finally, we demonstrate that the application of our methodology can enhance performance metrics in a large ambulatory care center in Canada. We show that a template-based scheduling rule can result in high resource utilization but poor scheduling decisions. However, an efficient scheduling policy equips a booking clerk with intelligent scheduling rules that are difficult for her to predict in real-time and work well in comparison to scheduling templates.