From Return on Investment to Return on Health: The CARE Framework for AI Governance, Accountability, and Equitable Transformation in Healthcare Systems

Abstract

<title>Abstract</title> In an era of digital acceleration, healthcare systems must evolve from prioritizing financial efficiency to optimizing health outcomes — transitioning from Return on Investment (ROI) to Return on Health (ROH). Yet, current marketing and communication analytics in healthcare often prioritize profitability over patient value, leaving a gap between technological capability and equitable health impact. This paper introduces the CARE Framework—an AI-governance and analytics system that integrates Causal Intelligence, Accountability Analytics, Responsible Engagement, and Equity Outcomes to realign data-driven decision-making with patient-centered objectives. By combining Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), and Incrementality Testing, CARE operationalizes accountability and transparency across healthcare communication systems. The framework transforms digital marketing and analytics functions into measurable tools for improving patient access, adherence, and affordability. Grounded in causal inference and ethical AI principles, CARE provides policymakers and healthcare organizations with a scalable pathway to implement responsible AI governance—where every optimized decision advances health equity, affordability, and system-level trust.

Ähnliche Arbeiten