Title: Dynamic Scheduling of Home Care Patients to Medical Providers
By Andre Cire. Jointly w.: Adam Diamant
Abstract: Home care aims at providing personalized medical care and social support to patients within their own home. It allows patients to avoid unnecessary hospitalization and either prevents or postpones institutionalization. Since 2014, it has been the fastest-growing US industry attending to more than 14 million patients per year. In this work we propose a dynamic scheduling framework to assist in the assignment of patients to home care practitioners (or HPs). An HP attends to the individual for the entirety of their care (continuity of care requirement) and must travel to their homes in order to serve them. We formulate the assignment of patients to HPs within a home care agency as a discrete-time Markov decision process (MDP). We consider the amount of service each HP provides per period, the expected number of remaining visits a patient will need with an HP, and the total time an HP spends in-transit serving their patient panel. Due to the curse of dimensionality and the complex underlying combinatorial structure of the problem, we propose a one-step policy improvement heuristic that builds upon the agencies existing assignment strategy. Specifically, we apply machine-learning techniques to learn different probabilistic policies from historical data, and formulate the one-step improvement problem as an exponentially-sized mathematical programming model. Such a model can be solved using L-shaped approaches that simultaneously provides upper and lower bounds at each iteration. We derive new relaxations to speed-up the convergence of our method and show sufficient conditions under which this relaxation be solved efficiently. Several extensions account for patients that return for service, multiple HP assignments per patient, and patients who need periodic service are also provided. We test the quality of our solution methodology with data from a Canadian home health care provider to assess the service improvement as compared to their existing policies.
Title: Surcharges Plus Unhealthy Labels Reduce Demand for Unhealthy Menu Items
By: Avni Shah. Jointly w.: James R. Bettman, Peter A. Ubel, Punam Anand Keller & Julie A. Edell
Abstract: Three laboratory experiments and a field experiment in a restaurant demonstrate that neither a price surcharge nor an unhealthy label are enough on their own to curtail the demand for unhealthy food. However, when combined as an unhealthy label surcharge, they reduce demand for unhealthy food. We also show that the unhealthy label is equally effective for women as the unhealthy label surcharge but backfires for men, who order more unhealthy food when there is an unhealthy label alone. We demonstrate that an unhealthy surcharge, which highlights both the financial disincentive and potential health costs, can significantly drive healthier consumption choices. From a policy and government perspective, if the goal is to reduce demand for unhealthy food, increasing the transparency of the health rationale for any financial disincentive is a necessity in order to effectively lower unhealthy food consumption.
Title: Redesigning the Emergency Department (ED): Lessons from ED at Southlake RHC
By Dmitry Krass. Jointly w.: Opher Baron, Marko Duic, and Tianshu Lu
Abstract: The emergency department (ED) of Southlake Regional Health Centre (Southlake RHC) started a waiting time management project in June, 2011. Since then, the time to physician initial analysis (TPIA) has dropped significantly. In this report, we document the key changes and empirically investigate their impact on four key performance measures: 90th percentiles of TPIA, and the lengths of stay of admitted patients (AdmLOS), non-admitted acute patients (AcuLOS), and non-admitted non-acute patients (NonAcuLOS). We use daily scale data on volume, resource capacity, and seasonal fluctuation. Based on our statistical analysis, we find that TPIA is decreased by nearly two hours; AdmLOS is decreased by more than five hours,; AcuLOS drops one hour when TPIA effect is taken into account; NonAcuLOS drops 15 minutes when TPIA effect is taken into account. Moreover, the total number of patient treated in the ED increased by 23.9% since the project started and until June, 2016. Our findings support the implementation of similar projects to improve the waiting time management in other EDs.