Integrating Predictive Modeling with Policy Interventions to Address Fraud, Waste, and Abuse (FWA) in U.S. Healthcare Systems

Abstract

Fraud, waste, and abuse (FWA) in the U.S. healthcare system are a persistent and expensive issue, draining tens to hundreds of billions of dollars every year and reducing the quality and integrity of healthcare provision. Conventional countermeasure mechanisms have depended primarily on retrospective audits, rule-based approaches, and law-based enforcement mechanisms, which tend to identify inappropriate behavior only after significant funds have been lost. Predictive modeling and  analytics have emerged as powerful instruments in recent years for the detection and prevention of FWA at an earlier phase by unveiling anomalous patterns and risky entities within extensive healthcare datasets. Predictive analytics is not adequate to fully mitigate FWA, however, without accompanying policy and organizational reforms facilitating the conversion of analytic findings into actionable steps. This research attempts to fill this gap. This work takes a conceptual exploration into how predictive modeling may be strategically integrated with policy interventions to create a robust, systems-level strategy for combating FWA in US healthcare. We discuss the scope and nature of FWA, review the strengths and limitations of predictive modeling techniques for this effort, and consider the range of policy levers—from payment reforms to regulatory action—that target FWA. We then propose an integrated approach that coordinates data-driven predictive analytics with preemptive policy interventions, thus enabling real-time prevention, adaptive deterrence, and continuous system improvement. Through an in-depth strategic and structural analysis, the article explains how this integrated framework can enhance detection effectiveness, deter fraudulent behavior by adjusting incentives, and address inefficient practices without discouraging legitimate care. The discussion is system-oriented and theoretical, describing key elements, interactions, and considerations for the successful alignment of technological and policy-based solutions in driving sustainable reductions in healthcare FWA.