AI-DRIVEN PREDICTIVE RISK MODELING TO IMPROVE COST EFFICIENCY AND SYSTEM RESILIENCE IN U.S. HEALTHCARE INFRASTRUCTURE

Abstract

The United States healthcare system faces unprecedented challenges related to escalating costs, resource allocation inefficiencies, and systemic vulnerabilities exposed by recent public health crises. This research explores how artificial intelligence-driven predictive risk modeling can transform healthcare infrastructure by enhancing cost efficiency and system resilience. Traditional reactive approaches to healthcare management have proven inadequate for addressing complex, interconnected risks that span clinical, operational, and financial domains. We propose an integrated AI framework that leverages machine learning algorithms, real-time data analytics, and predictive modeling to identify vulnerabilities before they escalate into crises. Our research examines current risk modeling practices in U.S. healthcare systems, identifies critical gaps in predictive capabilities, and develops a comprehensive framework for implementing AI-driven solutions. The study demonstrates that predictive risk modeling can reduce preventable hospital readmissions by up to 25%, decrease operational costs by 18%, and improve resource allocation efficiency during demand surges. By analyzing patterns across clinical outcomes, supply chain dynamics, and financial performance, the proposed AI systems enable healthcare organizations to shift from reactive crisis management to proactive risk mitigation. This work contributes both theoretical frameworks for understanding healthcare system vulnerabilities and practical implementation strategies for AI-driven predictive analytics.