From Fragmentation to Foresight: Reimagining Enterprise Prepay Integrity

The Next Generation of Payment Integrity Will Be Defined by Connected Intelligence

Foreword

Payment integrity is entering a new era. For more than a decade, payers have invested in prepay and post-pay controls designed to reduce inaccurate claims, identify fraudulent activity, and improve payment accuracy. Those investments have produced meaningful value, but they have also created a fragmented operating environment. Claims teams, clinical teams, provider teams, contract teams, utilization management, and payment integrity functions often work on the same risk from different angles, using different systems, timelines, and decision rules.

That fragmentation is increasingly difficult to sustain as the payment landscape gets more complex, more data-driven, and more exposed to regulatory scrutiny.

The scale of the challenge is visible in both government and Commercial markets. CMS reported a Medicare Fee-for-Service improper payment rate of 6.55%, representing $28.83 billion in improper payments in FY 2025.¹ Commercial markets face the same underlying pressures even if they are measured differently.  Industry sources estimate that 3% to 15% of healthcare claims are still being inaccurately paid.² The exact number varies by population, but the message is consistent: payment integrity remains a material enterprise issue across the payer landscape.

Enterprise Prepay Integrity represents the next phase of that evolution. Rather than adding more point solutions, the focus shifts to connecting the intelligence already present across the organization into a coordinated framework that can identify and address risk before payment is finalized.

The Evolution of Prepay Integrity

Most payers already use some combination of prepay solutions including claim edits, clinical review, coding validation, fraud detection, provider analytics, and contract compliance controls. These capabilities have been built over time to solve specific problems and reduce specific risks, and often each solution works well on its own.

The challenge is that payment risk rarely lives in one lane. A claim that appears reasonable from a coding perspective may still lack clinical support. A service that meets medical necessity criteria may still be misaligned with contract terms. A provider pattern that looks acceptable at the individual claim level may reveal a broader risk trend when viewed across an episode, specialty, or network segment. When these services are managed independently, the result is often duplicative efforts and missed opportunities.

Most payers arrived at their current PI state incrementally. As new risks were identified, new solutions were added. Clinical review was deployed to validate medical necessity. Early detection tools were introduced to flag suspicious billing behavior. Provider analytics were built to monitor utilization and identify outliers. Contract oversight functions were layered in to ensure reimbursement accuracy. Each addition was logical. The problem is that the cumulative model became more complex without becoming more connected.

The next stage of prepay integrity is better orchestration. Payers need a model that can combine multiple signals before payment occurs so that decisions are based on a more complete view of risk.

From Point Solutions to Enterprise Design

Enterprise Prepay Integrity is an operating model that brings together multiple forms of intelligence so that the organization can evaluate payment risk early and in context, not in isolation.

Five critical intelligence streams help to ensure success:

• Claims intelligence: Which identifies coding patterns, utilization trends, billing anomalies, reimbursement outliers, and historical payment behavior.
• Clinical intelligence: Which validates medical necessity, diagnosis and procedure alignment, documentation support, and appropriate utilization.
• Provider intelligence: Which examines billing patterns, specialty-specific behavior, emerging risk indicators, and network performance.
• Contract intelligence: Which ensures claims are evaluated against reimbursement methodology, fee schedules, contract terms, and payment rules.
• Payment intelligence: Which includes duplicate detection, coordination of benefits validation, eligibility verification, DRG validation, and other foundational accuracy controls.

Most payer organizations are fragmented because payment integrity has evolved through these specialization areas. Each function was designed to address a legitimate business need. Over time, however, the functions became optimized for their own workflows rather than for the enterprise as a whole.

That creates several recurring problems:

• The same claim may be reviewed multiple times.
• Different teams may reach different conclusions on similar cases.
• Opportunities for early intervention may be missed.
• Providers may receive inconsistent signals.
• Administrative overhead can grow as review layers multiply.

The cost of this fragmentation is not limited to operational inefficiency. It also affects provider experience and enterprise agility. When review activity is disconnected, providers can face unnecessary abrasion. Internal teams spend time reconciling differences rather than preventing errors. Leadership may have less confidence that the organization is seeing risk in a unified way.

The question, then, is not whether point solutions have value. They do. The question is whether they are working together in a way that maximizes enterprise value.

The power of an integrated model is not beholden to any single input. It is in how the inputs reinforce one another. A clinical concern can sharpen the priority list for payment review. A provider pattern can inform where additional scrutiny should be applied. Contract intelligence can reduce inconsistency in reimbursement interpretation. Payment findings can improve future rules and predictive models.

In a connected model, the payer no longer asks each team to solve the same problem separately. It creates a shared view of risk and coordinates the action of teams before payment occurs.

Market Forces Accelerating Change

Several market forces are also pushing payers toward a more integrated prepay model.

First, payment error and improper payment pressure remain substantial. Government program data makes that clear, but the issue is not confined to public programs. Commercial payers experience the same fundamental drivers: coding complexity, documentation gaps, contract variation, billing mistakes, and inappropriate or abusive payment behavior. The measurement methods may differ, but the exposure is real across populations

Second, AI and automation are increasing the importance of governance. As organizations deploy machine learning, predictive models, and AI-assisted workflows, they must ensure those tools are explainable, auditable, and aligned with clinical and payment policy. Automation can improve speed and consistency, but only if the underlying operating model is strong enough to support it.

Third, interoperability and prior authorization modernization are expanding the availability of structured data earlier in the care and payment cycle. That matters because the timelier and more connected the data, the more opportunity exists to intervene before a claim is paid. Payers that can harness that information will have a structural advantage.

Fourth, transparency expectations continue to rise. Regulators, employers, members, and providers all expect payment decisions to be more defensible and easier to explain. A fragmented system makes that harder. An enterprise model makes it more achievable.

The combined effect of these forces is clear: payment integrity must move upstream and become unified.

Building an Enterprise Prepay Strategy

Enterprise Prepay Integrity is a strategy that aligns data, analytics, clinical expertise, provider intelligence, and payment oversight around a common objective: prevent inaccurate payment before it occurs.

Payment integrity maturity will progress through a clear sequence.

The strategic shift is important. Recovery is necessary, but it is not enough. Prevention is better, but it is still limited if intelligence is fragmented. Prediction is stronger, but it becomes most powerful when the enterprise can act on it consistently. Enterprise Prepay Integrity represents the highest maturity stage because it connects the information, the people, and the workflows needed to prevent inaccurate payment before it happens.

Several capabilities are foundational to build the enterprise model for the Enterprise Prepay Intelligence stage:

• Integrated data – visibility across claims, clinical, provider, contract, and payment data. Without that foundation, each team continues to operate from a partial picture. A unified data environment makes it possible to understand risk in context and drive consistent decisions.
• Advanced tech- predictive analytics and AI should not replace judgement but help direct it by identifying risks before payment. More advanced models can prioritize review, reveal emerging patterns, improve resource allocation, and focus human expertise where it will have the greatest effect.
• Clinical validation – clinical expertise remains essential. The most effective programs use targeted review to focus scarce clinical resources where risk indicators suggest a meaningful chance of inaccuracy.
• Coding and reimbursement accuracy – Accurate reimbursement begins before claims are finalized. DRG assignment, coding accuracy, documentation support, and reimbursement methodology should all be evaluated as early as possible. Waiting until after payment increases recovery cost and reduces the likelihood of preventing recurrence.
• Continuous intelligence – Mature organizations do not treat each intervention as a one-time event. They use the results to improve process around future detection, future review logic, and future payment decisioning. That feedback loop is what turns prepay integrity from a set of controls into a learning system.

 A Balanced View Across Payer Types

Payer organizations are not all structured the same way and all have unique needs. Some serve primarily Medicare and Medicaid populations. Others are heavily Commercial member focused. Many operate in a blended environment across government and Commercial lines of business. The right strategy should work across that entire spectrum.

For government programs, CMS data provides a measurable benchmark and underscores the scale of improper payment exposure. For Commercial plans, the issue shows up in claims accuracy, fraud, waste, abuse, coding variation, and reimbursement complexity. For blended organizations, the challenge is both broader and more urgent because the enterprise must manage multiple payment environments at once.  That is why Enterprise Prepay Integrity is a useful framing. It is not tied to one population or one line of business. It is a model for any payer that wants to improve payment accuracy, reduce waste, and make better use of the intelligence already inside the organization.

Summary

The future of payment integrity will not be defined by who has the most edits, the biggest audit team, or the largest collection of point solutions. It will be defined by who can connect intelligence across the enterprise and act on it before payment occurs. That is the real shift that is underway.

Enterprise Prepay Integrity is the framework for that future. It brings together claims, clinical, provider, contract, and payment intelligence into one coordinated approach, helping payers make more accurate decisions in real time. The ultimate objective is not simply additional edits or reviews; it is a smarter, more connected ecosystem where accuracy is designed into a framework.