Overview
This project applies stochastic optimization and data-driven modeling to improve the design and operation of emergency and healthcare systems. Current work examines ambulance fleet composition and dispatch strategies under uncertainty, identifying where to locate different ambulance types to minimize response times and improve patient outcomes. Ongoing work extends this to emergency department (ED) utilization patterns, examining how patient flow, resource constraints, and system design interact to drive ED overcrowding and poor patient outcomes. Together, these projects aim to give healthcare systems engineers practical tools for more effective and efficient service delivery.
Publications
Journal Papers
A stochastic programming approach for locating and dispatching two types of ambulances
S. Yoon, L.A. Albert, and V.M. White
Transportation Science,
vol. 55, no. 2, pp. 275–296,
2021.
https://doi.org/10.1287/trsc.2020.1023
Classification of opioid usage through semi-supervised learning for total joint replacement patients
S. Lee, S. Wei, V.M. White, P.A. Bain, C. Baker, and J. Li
IEEE Journal of Biomedical and Health Informatics,
vol. 25, no. 1, pp. 189–200,
2020.
https://doi.org/10.1109/JBHI.2020.2992973
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