Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion

The development of predictive models in healthcare settings has been growing; one such area is the prediction of patient arrivals to the emergency department (ED). The general premise behind these works is that such models may be used to help manage an ED that consistently faces high congestion. In this work, we propose a class of proactive policies that utilize future information of potential patient arrivals to effectively manage admissions into an ED while reducing waiting times for patients who are eventually treated. Instead of the standard strategy of waiting for queues to build before diverting patients, the proposed policy utilizes the predictions to identify when congestion is going to increase and proactively diverts patients before things get “too bad.” We demonstrate that the proposed policy provides delay improvements over standard policies used in practice. We also consider the impact of errors in the information provided by the predictive models and find that even with noisy predictions, ou...