THE PERILS OF PREDICTIVE ANALYTICS: HOW AI DISTORTS INCENTIVES IN VALUE-BASED CARE MODELS

Abstract

The rapid integration of artificial intelligence (AI) and predictive analytics into value-based care (VBC) models promise enhanced efficiency, improved outcomes, and reduced costs. However, this research paper identifies a critical paradox: while AI technologies ostensibly support VBC objectives, they simultaneously introduce perverse incentives that undermine the fundamental principles of value-based reimbursement. Through a comprehensive analysis of algorithmic bias, data limitations, and financial motivations, we demonstrate how predictive analytics can systematically distort clinical priorities, exacerbate health disparities, and compromise care quality in VBC arrangements. Our findings reveal that without robust governance frameworks and technical countermeasures, the convergence of AI and VBC risks creating self-reinforcing cycles of inequity and inefficiency. We propose a multidimensional solution integrating technical standards, policy reforms, and ethical frameworks to realign AI-powered VBC with its original quadruple aim principles.