Session | ||
MB2 - HO7: Data-driven optimization and personalization in healthcare
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Presentations | ||
Geographic virtual pooling of hospital resources: data-driven tradeoff between waiting and traveling 1CUHK Shenzhen, China; 2University of Waterloo, Canada; 3Northwestern University, United States 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. Policy optimization for personalized interventions in behavioral health 1New York University; 2University of Wisconsin Madison; 3MIT 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. Operational challenges in emergency service platforms in developing countries 1University of Toronto, Canada; 2Georgia Institute of Technology; 3Erasmus University Rotterdam; 4Kühne Logistics University; 5Flare 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. |