The importance of a strong postpay data mining program

By Chris Stanley, Sr. Director of Client Services Management, Data Mining and COB

Prepay edits alone are not enough to capture a large variety of overpayment scenarios. A strong payment integrity strategy requires both prepayment controls and post payment data mining audit capabilities. 

Why prepay and postpay strategies are needed

Prepay edits are critical for stopping errors before funds are disbursed, but are inherently limited by timing, data availability, and a need to avoid disrupting provider workflows. Relying solely on prepay edits will leave meaningful overpayment exposure unaddressed, but an effective postpay data mining strategy provides a safety net to address this exposure.

In 2025, Cotiviti estimated that 86% of the overpaid claims we identified for our data mining customer base would not have been found through existing prepay claims editing.

How prepay edits alone can fall short

Prepay edits operate in real time, with adjudication based solely on the data available at the time of submission. While effective for identifying clear, rule-based issues, this approach inherently limits the ability to detect more complex or pattern-based overpayments. To support prompt-pay requirements and efficient processing, prepay edits must be narrowly targeted and applied with a high degree of confidence. As a result, certain overpayment scenarios are less likely or impossible to be captured up front.

These constraints give rise to several key challenges: 

• Limited information at the time of adjudication: When a claim is processed, almost invariably only that individual claim is reviewed. Many overpayments can only be identified by looking at patterns across multiple claims, providers, or over a period of time, which isn’t always possible in real time.

• Provider abrasion concerns: Payers often limit prepay edits to avoid potential issues that could lead to false positives causing unnecessary denials, rework, and provider dissatisfaction.

• Operational constraints: Real-time adjudication environments are designed for efficiency, which limits the complexity and breadth of logic that can be applied during initial claim processing.

Where postpay adds critical value

Postpay data mining programs do not operate with the same prepay constraints.

Deeper, retrospective analysis (driven by broader datasets, more extensive pattern detection, and advanced analytics) is especially effective for:

1. Pricing discrepancies and contract misapplication

2. Duplicate billing over time

3. Inconsistent billing for high-cost services

4. Global billing and episode of care issues

5. Pharmacy unit billing that requires provider outreach

Let’s take a closer look at postpay data mining’s impact on each of these.

Pricing Discrepancies

Complex contract logic and frequent updates can be difficult to execute in real time. Claims may be paid using incorrect fee schedules, outdated contract terms, or misapplied modifiers. Postpay data mining’s retroactive validation helps payments align with the most current contract terms and pricing rules.

Duplicate Billing 

A provider may submit similar claims in a short period, or claims may be submitted to pharmacy or dental plans in addition to medical plans. Typically, prepay edits only check within a narrow window for exact matches. Postpay data mining can identify patterns of duplication across longer timeframes, providers, and benefit plans.

Inconsistent Billing for High-Cost Services

Expensive services such as surgical procedures or implantable devices can be billed differently based on codes, units, or how charges are submitted. With only prepay edits in place, it can be difficult to check every possible variation in real time without slowing claims processing or creating unnecessary denials. Postpay data mining retrospectively looks at many similar claims to better detect unusually high billing.

Global Billing

Services included in a contracted global payment (like case rates) may be billed separately. Only using prepay edits requires full visibility into prior and related services within the episode of care. Postpay data mining reviews the episode retrospectively to identify improper additional billing.

Pharmacy Unit Billing

With only prepay, it can be difficult to determine billed unit appropriateness without additional context from the provider. Denial or adjustment in real time risks inaccuracies and provider abrasion. Postpay data mining reviews claims over time and compares them against expected dosing ranges or standard billing benchmarks. This analysis can flag claims that are potentially overpaid, while creating the basis for targeted provider outreach to determine how units were calculated and billed.

Key takeaways for payers

Prepay and postpay strategies should be viewed as complementary. Prepay edits are crucial in acting as a frontline defense, preventing clear and immediate errors. Postpay data mining functions as a potential safety-net, utilizing artificial intelligence, machine learning, advanced analytics, and pattern detection to retrospectively capture more complex, nuanced, and evolving overpayment scenarios that cannot be reliably identified in real-time. 

Consider these next steps:

• Ensure your operation is not allowing leakage in its overpayment review process by taking into consideration the complex, nuanced opportunities that are highly challenging, if not impossible, to capture with prepay edits alone. 

• Prioritize analytics that evaluate patterns across time and providers, rather than relying solely on claim-level edits.

• Establish a formal feedback loop to appropriately convert postpay audit findings into targeted prepay edits based on complete data made available at the time of adjudication for improved prevention without increasing provider abrasion. 

Reach out to learn how we’re helping health plans like yours drive greater payment accuracy with postpay data mining.

About the author

Chris Stanley supports Cotiviti’s Payment Accuracy suite through oversight of our postpayment client services management and operational reporting functions, helping ensure consistent delivery, clear insights, and measurable results.  With extensive experience across the healthcare landscape, Chris brings a well-rounded perspective shaped by work on both the payer and provider sides.  This dual vantage point enables him to understand the complexities and competing priorities within the healthcare reimbursement environment, and to deliver practical, balanced solutions that drive accuracy while supporting strong provider and client relationships.