The economics of earlier

Healthcare payers have spent decades getting better at finding errors—but the next economic advantage comes from preventing those errors before they become paid claims, recovery projects, provider friction, or member disruption.

In healthcare payment operations, time changes the economics of an error. An issue identified before a transaction is completed can often be handled as part of normal workflows. The same issue discovered after payment becomes a different kind of problem. It can trigger investigation, outreach, recovery, reconciliation, appeals, provider rework, member questions, and additional reporting. The original payment error may be the same, but every step added after the fact increases its operational burden.

Healthcare payment errors are rarely caused by a lack of data. More often, they result from information arriving too late, residing in disconnected systems, or reaching decision-makers after the opportunity to act has passed.

That is why the real question is no longer simply, “Can we detect it?” The more valuable question is, “How early can our infrastructure catch it and correct it to support the best outcome?” A day of delay can mean one more transaction processed with incomplete information, one more exception handed to a downstream team, or one more opportunity for avoidable cost to become embedded in the system.

Prevention changes the cost curve

Prevention beats recovery regardless of claim type or moment in the lifecycle. It reduces the need to unwind decisions that have already moved through multiple systems and stakeholders. It also helps preserve trust by making accuracy part of the transaction itself, rather than a correction imposed later.

In data mining, for example, Cotiviti data suggests that 5-10% of postpay audit value can shift to fully automated prepay interventions, with an additional 25-40% addressed through prepay pause-and-review workflows when reliable data is available.

A combined prepay and postpay strategy can increase total savings by 30% or more while reducing provider abrasion, administrative costs, and recovery efforts. By identifying issues before payment, health plans can accelerate time to value and avoid the delays, overhead, and burden associated with postpay recovery.

This does not eliminate the need for retrospective review. Recovery will remain important for complex cases, historical issues, and patterns that only become visible over time. But it should not be the default operating model when the data and intelligence needed to act earlier are available. The strongest payment integrity strategy is layered: establish accurate information as early as possible, apply policy and clinical intelligence at the point of decision, and use post-payment analysis as a focused backstop rather than the first line of defense.

The earliest decision may happen before the claim

Some payment errors begin upstream, before a claim even exists. Coverage changes, incomplete information, and outdated records can affect which payer should pay first. If those discrepancies are not resolved during enrollment and ongoing eligibility maintenance, they can travel silently into claims processing. By the time the issue is found, payment may already have been made and the health plan may be managing a recovery rather than preventing an error.

Moving intelligence to enrollment changes that equation. Continuously validating coverage signals and maintaining accurate order-of-benefit information can prevent an avoidable payment at its source.

It also makes the economics of earlier especially clear: catching a discrepancy at enrollment is worth more than catching it after adjudication, because the earlier intervention avoids both the inaccurate payment and the work required to reverse it. Cotiviti predicts that 20–30% additional savings can be achieved from a pre-claim coordination of benefits (COB) program compared to postpay recovery alone, with $3–$5 in overpayment per member per month coming from improper claim avoidance. And this proactive approach to COB could potentially also see a 30% or more improvement in accuracy of identifying COB issues, with an estimated 40% reduction in COB-related administrative costs.

Real time must extend across every claim type

A modern payment accuracy engine cannot stop at the boundaries of medical claims. Dental and pharmacy transactions demonstrate why. Each has distinct policies, clinical considerations, coding or benefit complexity, and fraud, waste, and abuse risks. Yet, the infrastructure requirement is the same: connect the right data, apply the right intelligence, and make a transparent decision before payment whenever possible.

The economics improve further when data is captured once and reused across multiple workflows, reducing duplication while expanding the value of each integration. 

Extending a common infrastructure layer across claim types allows healthcare organizations to reuse established workflows, integrations, policy management practices, analytics, and reporting instead of assembling a new point solution for every category.

Dental and pharmacy are therefore not exceptions to an enterprise approach; they are proof that a scalable accuracy engine can accommodate specialized expertise while preserving a consistent operating model.

For dental claims, that model can combine configurable policy logic, clinical validation, documentation review, analytics, and clear explanations of why a claim was flagged. For pharmacy transactions, it can connect medical and pharmacy information that often resides in separate systems, then apply clinically informed logic while the transaction is still in motion. In both cases, earlier intelligence improves precision and creates a clearer, more defensible path from policy to payment.

Real-time infrastructure is more than speed

Every scaled industry eventually builds shared infrastructure to coordinate increasingly complex transactions. Healthcare is now reaching the same inflection point.

“Real time” should not be reduced to a faster rules engine. Today’s operating standard requires connected data, embedded intelligence, workflow integration, transparency, and governance. Speed without context can simply produce a wrong answer faster. The infrastructure must be able to interpret signals, apply policy consistently, incorporate expert knowledge, and show why a decision was made.

Artificial intelligence strengthens this model when it is embedded in workflows. It can help prioritize discrepancies, interpret complex coverage scenarios, support policy development, and connect information that would otherwise remain fragmented. Human expertise remains essential for clinical judgment, policy governance, and the cases that require nuance. The advantage comes from combining both so that intelligence is available at the earliest practical moment, and every decision can be reviewed and explained.

Measure what was prevented, not just fixed

Payers often measure payment integrity results through identified savings and recoveries. Those measures matter, and an earlier operating model expands the savings opportunity. Leaders should also examine inaccurate payments avoided, recovery activity prevented, manual touches reduced, cycle time improved, provider rework avoided, and the share of decisions resolved before payment. Cotiviti estimates that adjusting claims can cost $2–$5 per claim for simple, straightforward corrections, while complex adjustments requiring manual review from denied or rejected claims can cost $25–$117 per claim.

This is the economic value of infrastructure: not one isolated edit, but a repeatable capability that lowers friction across many transactions and lines of business.

As more workflows move onto a connected foundation, each new use case can benefit from existing integrations, data, policy controls, reporting, and decision support. The return compounds because the payer is not rebuilding the operating model each time it addresses a new claim type or lifecycle moment.

Make prevention the default

The shift to real-time infrastructure is ultimately a shift in operating philosophy. Instead of accepting fragmentation and correcting its consequences later, healthcare organizations can move intelligence closer to the origin of each decision. They can validate coverage before claims are submitted, apply claim-specific expertise before payment, and connect information across systems while action is still possible.

Real-time infrastructure is ultimately about coordination.

When information reaches the right participant at the right moment, healthcare organizations can prevent errors earlier, reduce administrative friction, and operate with greater confidence. 

The future of payment accuracy is not finding mistakes faster. It's building the infrastructure that makes fewer mistakes possible in the first place. The standard should be straightforward: prevent what can be prevented, explain every intervention, and reserve recovery for what truly could not have been known earlier. In that model, every day saved is more than a faster process. It is less avoidable spend, less provider and member friction, and a more accurate healthcare transaction from the start.

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