“Modeling and Analysis of Emergency Healthcare Operations” by Dr. Zhankun SUN
IIM SEMINAR
Speaker:
Dr. Zhankun SUN
Eyes High Postdoctoral Scholar of Operations and Supply Chain Management
Haskayne School of Business
University of Calgary
Abstract:
The talk consists of two parts: the first part is motivated by the emergency response in the aftermath of mass casualty incidents; the second part is one of my ongoing projects on healthcare operations analytics in hospital emergency departments.
In service systems, prioritization with respect to the relative “importance” of jobs helps allocate the limited resources efficiently. However, the information that is crucial to determine the priority of a job may not be available immediately, but can be revealed through some investigation. While investigation provides useful information, it also delays the provision of services. Therefore, it is not clear if and when such an investigation should be carried out. To provide insights into this question, we consider a service system with two types of jobs. The type of each job is unknown but the server has the option to perform investigation to determine the type of a job albeit with a possibility of making an incorrect determination. Our objective is to identify policies that balance the time spent on information extraction with the time spent on service. We use Markov decision process to formulate this problem and prove that the optimal dynamic policy can be characterized by a switching curve. One insight that comes out of this characterization is that the server should start with performing investigation when there are sufficiently many jobs at the beginning and never perform investigation when there are few jobs.
Part II: Prediction of Door-to-Doctor Wait Times in Emergency Department
A number of Canadian hospitals have started publishing live emergency department (ED) door-to-doctor wait times online in an effort to provide patients with expectations on how long they will have to wait to be seen for non-urgent care after initial assessment by a triage nurse. The goal of this project is to accurately predict the real-time ED wait times and study the impact of different prediction techniques on patient flow and patient care. We develop our predictor based on a stochastic model to account for the variability in the patient flow process in ED. With a case study at the four major hospitals in the Calgary area we illustrate the performance of our wait time predictions. We compare this with other predictors observed in call center settings as well as the current prediction method. More importantly, we use a simulation model to illustrate the impact of the online wait times on the patient flow within the Calgary area.
