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The Pre-pay gap is an execution
gap, not an intelligence gap

See how agentic AI turns fragmented payment integrity data into 
governed, real-time action, before a claim is ever paid.

Pre-payment integrity is stalling — Here's why

Health plans have invested in claims editing, clinical guidelines, contract logic, and provider data. The problem is these capabilities don't talk to each other. Pre-pay decisions must happen while the claim is still moving — requiring context from multiple systems, assembled in near real time. Instead, most health plans rely on manual queues that can't scale, disconnected point solutions, and static editing rules that miss clinical and contract context.

The consequence: stop too little and you miss leakage. Stop too much and you trigger provider abrasion, appeals, and compliance exposure. 77% of Medicaid improper payments in FY2025 were tied to insufficient documentation — not missing data, but an inability to act on it fast enough before payment cleared.

From fragmented detection to governed pre-pay execution

Closing the pre-pay gap requires a different execution model — not another point solution. Agentic Systems of Execution are that shift.

Built on top of your existing platforms, an agentic system of execution assembles clinical, coding, contract, and provider evidence at decision speed; applies policy and contract logic in context; routes the right action to the right pathway; and keeps human reviewers focused on high-value exceptions — all with an audit-ready decision trail at every step.

This is how payment integrity programs move from reactive post-pay recovery to proactive pre-pay prevention, without replacing the infrastructure health plans already depend on.

What governed pre-pay execution delivers

Reduced payment leakage

<p>Intervene before payment, not after. Identify and stop more improper payments earlier in the claim lifecycle.</p>

Less provider abrasion

<p>Precise, explainable interventions mean fewer unnecessary pends, edits, and documentation requests.</p>

Scalable review capacity

<p>Agentic evidence assembly frees clinical and coding reviewers to focus where human judgment matters most.</p>

Stronger compliance posture

<p>Every action is documented with rationale, confidence level, and a full audit trail.</p>
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Ready to close the pre-pay
payment integrity gap?

New Everest Group research maps the execution gap holding 
pre-pay programs back — and the practical roadmap to
governed pre-pay execution.

FAQs

What is pre-pay payment integrity?
<p>Pre-pay payment integrity identifies and prevents improper payments before a healthcare claim is paid, using clinical guidelines, coding logic, and contract terms during adjudication to detect errors, fraud, waste, and abuse before payment is released.</p>
Why is pre-pay harder to scale than post-pay?
<p>Post-pay review allows retrospective investigation with more time and flexibility. Pre-pay decisions must be made while the claim is still moving, requiring evidence to be assembled and acted on within compressed adjudication timelines that manual processes cannot reliably meet at scale.</p>
What is a System of Execution in payment integrity?
<p>A System of Execution is an orchestration layer that sits across existing claims platforms, policy repositories, and provider databases, coordinating evidence assembly, decision routing, human review handoffs, and audit documentation before a claim is paid.</p>
Does agentic AI require replacing existing payment integrity systems?
<p>No. Agentic Systems of Execution work on top of existing platforms, not in place of them. Health plans can deploy agentic capabilities incrementally, starting with high-value use cases like DRG review, itemized bill review, and FWA detection, expanding as controls mature.</p>
What is the scale of the improper payments problem in US healthcare?
<p>CMS reported approximately $96 billion in improper payments across Medicare, Medicaid, and CHIP in FY2025. Including commercial plans, Everest Group estimates the total rises by an additional $28–35 billion annually.</p>
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