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 which consistently faces high congestion. In this work, we propose a class of proactive policies which utilizes 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, our proposed policies can still outperform (achieving shorter delays while serving the same number of patients) standard diversion policies. If the quality of the predictive model is insufficient, then it is better to ignore the future information and simply rely on real-time, current information for the basis of decision making. Using simulation, we find that our proposed policy can reduce delays by up to 15%.
Xu, Kuang, and Carri Chan. "Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion." Columbia Business School, 2015.
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