Analytics for Managing Health Care Wait Lists: Predictive and Prescriptive Approaches

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Excessive wait times for care are a pressing problem in Canada. In 2004, the government invested $5.5 billion dollars in a “Wait Times Reduction Fund.” However, several reports make it clear that further significant reductions in wait times are still needed. The goal of this DG is to develop and apply predictive and prescriptive analytics to improve wait list management. As an application of our work, we will work with surgeons and administrators at BC Children's Hospital (BCCH) to improve the wait time experience for pediatric patients awaiting elective surgery. On the predictive front, we have:***Aim 1: Development of wait time prediction tools to provide patients with more accurate estimates of their anticipated wait times for surgery.***Since frustration with waiting often occurs when there is a mismatch between anticipated and actual wait times, Aim 1 intends to improve individuals' wait time experiences. Furthermore, accurate wait time estimation may help patients make better “wait or leave” queueing decisions, especially when they have outside options for care. ***We will apply the tools of queueing theory, simulation, and machine learning to develop wait time prediction tools for patients joining a surgical wait list. We have already developed a discrete-event simulation (DES) model of wait list dynamics at BCCH, which we will use for estimating wait times. However, it is computationally expensive to run the model every time a surgeon wants to provide a wait time estimate for a patient. Instead, we will run the DES offline to generate wait time samples of patients joining the wait list from various states, from which we will fit regression and machine learning models. The final predictive model will strike a balance between accuracy, transparency, and ease-of-use as a bedside tool. While there are publications describing wait time prediction models for emergency departments, we are unaware of predictive models for estimating wait times for surgery. ***On the prescriptive front, we have:***Aim 2: Identification of effective and practical strategies for pooling surgical wait lists. ***Traditionally, surgeons who conduct the initial consultation with a patient also perform the surgery itself. While this may be good from a continuity-of-care point of view, there are some procedures which may be reasonably performed by other surgeons with the same expertise. This raises the interesting prescriptive question of how best to design a system in which some surgeons pool their wait lists. ***We will work closely with surgeons to first identify potential patient types and surgeries that can be pooled, and then we will use our DES model in a simulation-optimization framework to identify promising system design changes. Our objective will be to reduce surgery wait times while also giving careful consideration to ease of implementation. This aim of the DG will also contribute to the process flexibility literature, by extending it to health care settings. **