Payment integrity is a system problem, not a data failure

A man will be shocked when he finds his insurance has denied the coverage of the key part of the hospitalization. When he looked at what was happening, he found a billing code that showed that his actions were selective. But that was not the case – he was accepted through the emergency room with severe chest pain. He was told that surgery was the only option.

So he called the hospital. They confirmed that the procedure was indeed medically necessary. However, due to a single incorrect entry – the system is not marked by a clinician when it is in the hospital – the system now considers his lifesaving care as an “optional”.

Errors are cascading from there only. Refusal to pay, triggering an appeal, and now, the man who has recovered from the surgery is trapped between clinical truth and administrative novels. From the payer's point of view, the claim looks reasonable. These codes are technically correct and the documentation is checked out.

This problem occurs only when the patient checks his bill carefully.

Now, multiply this event by millions of claims. The result is not only management overhead or confusion, but also systemic inefficiency. Wasting time to correct data, spending on addressing preventable disputes, trust between payers, providers and patients will be reduced. Still, many people stratify by tying more checks or outsourcing them to suppliers who promise to spot errors after they occur.

This approach only addresses the symptoms, not the root cause. It highlights a deeper structural problem: our healthcare system’s reliance on decentralized response processes rather than proactive system design.

Determine the problem

The main opponent behind claiming that inaccuracy is not bad behavior or wrong classification. The culprit is a decentralized system that cannot communicate well. As our healthcare system develops, the greater quality of care is provided for more (increasingly clinically complex), the disconnection of people, what is recorded and what is actually real has deepened.

That's why decades of digitalization and vendor optimization, administrative waste in healthcare still exceeds $1 trillion a year.

Historically, upstream solutions for payment errors have been considered unsolvable. The complexity of the system is well known to make automation difficult. There are over 700 diagnostic-related categories (DRG) categories, each with its own hierarchical severity and pricing logic. Medicare alone operated more than 30 payment plans last year, and billing rules vary widely between hospitals and health plans. Adding inconsistent clinical documentation and ambiguous policy language, the result is the same: manual reconciliation of information that should be aligned from the outset.

The main argument over the years is that these issues are data-centric. Better data, more audits and even more code will solve the challenges of payment integrity. However, there is no amount of raw data to fix the process of fundamental flaws. The data is not aligned and the intelligent workflow will only generate more noise.

Health planning has never had a tool to intelligently leverage noisy data on a large scale. But technology has changed. Today’s AI systems can understand language, follow policy logic, and evaluate complex clinical and contract data in real time. Just like you want a second opinion among doctors before a major procedure, both patients and payers should be given a system that doubles the critical decisions before causing problems downstream.

The rest of the obstacles are cultural, not technical. Too many organizations still believe that payment integrity must be responsive. Disputes are seen as inevitable. Errors are problems to be solved in the future, not prevention now. But this assumption is outdated.

Adopt active payment method

With AI’s LightSpeed ​​progressing, healthcare providers and health plans can now have tools to ensure payment accuracy from the start. Smart systems can be trained to understand the full picture of members’ care and billing journeys, from their policies to their records to their contractual requirements. Humans will always be an important part of the process, with AIs able to quickly approve and potential inconsistencies with backgrounds related to human experts.

This is not to replace people. This is a second opinion about giving clinicians and claiming that the team is equivalent to real-time – not only will it find errors, but it can also prevent them from affecting the patient’s experience.

As we move from an era of “intellectual scarcity” to “intellectual richness,” we have the opportunity to rethink the second opinion on how to leverage AI with greater benefits.

For cardiac surgery patients, an AI-driven system will immediately mark the wrong elective code and compare it with clinical documents, admission types, and policy rules. This inconsistency would have been seized and corrected before a claim is filed to prevent expensive denials, long-term appeal proceedings, and intense pressure from people trying to be healthy.

These AI systems work by integrating data flows (clinical, financial, policy) and applying advanced logic continuously rather than retrospectively. By aligning these inputs, they enable the ecosystem to “correctly for the first time”, avoiding expensive denials and re-cycling.

This transformation does not require a comprehensive overhaul of the system. It requires clear and consistent use of AI as a tool for intelligent amplification and improved accuracy.

Building these technologies is a heavy lifting, but the most difficult requirement will be cultural. Organizational consistency is required to allow information to flow across departments and systems. Isles (regardless of data, departmental or process-driven) must be eliminated. Clinical and financial logic should work together, not in isolation.

This is how we move from a reactive payment system to an active one. By ensuring the accuracy of the system entering the system, we eliminate the need to clean up the contents in it and eliminate the fear, chaos and waste associated with incorrect claims.

Photos: lbodvar, Getty Images

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