Leveraging AI for Lowering Patient Financial Responsibility in the USA Healthcare

Abstract

In the United States, patient out-of-pocket (OOP) costs, including deductibles, copays, coinsurance, and outstanding balances, have increased sharply over the past few decades. This surge is driven mainly by administrative waste, billing coding errors, inefficient care pathways, and rising premiums. Despite huge investments at the national level in digital health infrastructure, patients continue to face unnecessary financial burdens due to scattered information, complex eligibility rules, and billing/coding errors. This paper examines how Artificial Intelligence (AI) and Machine Learning (ML) can help reduce patient costs by focusing on two main drivers of high costs: administrative waste and clinical inefficiency. Through a detailed review of AI applications in increasing administrative efficiency, enhancing cost transparency, optimizing Revenue Cycle Management (RCM), and predictive analytics, this research shows that AI-driven process optimization can significantly reduce healthcare expenses. The research collects evidence from federal agencies, studies, and industry deployments to support how AI can significantly lower patient costs while improving clinical and operational efficiency. This paper also discusses the challenges and complications of AI deployment in the digital healthcare system.