Patient-level data from 72 MRI hospitals in Ontario, Canada from 2013 to 2017 shows that over 60% of patients exceeded their wait time targets. We conduct a data-driven analysis to quantify the reduction in the patient Fraction Exceeding Target (FET) for MRI. Our resource pooling model lowers the FET from 66% to 36% while constraining the average incremental travel time below three hours. In addition, our model and method show that only ten additional scanners are needed to achieve 10% FET.

Problem: We optimize personalized interventions for behavioral health using digital platforms, considering cost and capacity constraints. Existing approaches are data-intensive or overlook long-term dynamics. Our DecompPI algorithm approximates policy iteration, reducing intervention costs while maintaining efficacy. A case study shows potential for 50% cost reduction, enabling scalable implementation.

Many developing countries lack the health-emergency infrastructure of the developed world. In this context, our industry partner Flare (operating in Nairobi, Kenya) coordinates existing ambulance providers by operating a platform. We study the operational challenges for such platforms as they often lack knowledge about all ambulances' future availability and their location at a tactical level and typically do not fully control these ambulances.