Improving Healthcare Resource Allocation within a Geographic Area through Optimization and Machine Learning

Abstract

Healthcare policymakers face critical challenges in resource allocation that require: (1) practical decision-support tools, (2) robust prescriptive models that account for uncertainty, and (3) integration of multiple data sources for predictive analytics. Addressing fairness and social vulnerability is particularly crucial in healthcare resource allocation decisions. This thesis presents novel optimization and machine learning methodologies to tackle these challenges in resource-limited healthcare settings, including point-of-care testing allocation in western Kenya and mental health prevalence estimation in Washington state. The primary contributions include practical optimization models for real-world healthcare resource allocation and innovative data fusion approaches for enhanced decision-making. These methodologies provide healthcare policymakers with critical insights for informed, equitable, and effective healthcare planning decisions. This research comprises three interconnected components that comprehensively address healthcare resource allocation challenges. The first component develops decision-support tools for the strategic placement of point-of-care HIV viral load and drug resistance testing machines in Kisumu County, Kenya, optimizing resource distribution in high-need areas. The second component formulates and solves a queueing-location-allocation model using integer programming and Conditional Value at Risk (CVaR) to manage demand uncertainties in testing samples, enhancing healthcare delivery system resilience. The third component advances data fusion techniques by integrating optimization and machine learning to estimate pediatric mental health prevalence in Washington state, merging multiple national surveys with local datasets. Together this work enables healthcare policymakers to make evidence-based and strategic resource distribution decisions.