For years, healthcare organizations have approached Medicare fraud and billing compliance as primarily a retrospective exercise. Claims are submitted, payments are made, audits follow, and organizations investigate problems after they surface.

The federal government is moving toward a more aggressive approach to Medicare program integrity, using data analytics, artificial intelligence (AI), and machine learning to spot suspicious billing patterns earlier and, in some cases, prevent improper payments before they are made.

CMS has made the direction clear. In August 2026, the agency said its enforcement actions had stopped more than $1.6 billion in potentially improper Medicare laboratory payments since the beginning of the Trump administration. The effort included using AI and machine-learning models to analyze Medicare fee-for-service claims, identify unusual billing patterns, and flag high-risk claims for further review.

In September 2026, CMS announced another major enforcement action involving 11 medical equipment suppliers associated with more than $3.4 billion in suspected fraudulent Medicare Advantage and Part D billing. The agency said data analytics helped identify suppliers billing for equipment involving deceased beneficiaries and other suspicious activity.

This is more than an escalation of traditional fraud enforcement. It’s a fundamental change in the timing of the fight. The question for healthcare organizations is no longer simply whether they can respond effectively when an auditor finds a problem. It is whether they can identify the same risks before increasingly sophisticated government enforcement does.

The traditional model of program integrity has often been characterized as “pay and chase,” which involves identifying a claim, paying it, discovering a problem later, and attempting to recover the money.

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