Healthcare's Rearview-Mirror Payment Model Is Broken. Decision Intelligence Is the Solution

By Brian Berkowitz, Chief Product Officer, Lyric

For most of its modern history, the healthcare payments industry has operated in reverse. Errors are caught months after care has been delivered, when costs have already compounded, leaving patients, providers, and payers to untangle mistakes that were entirely preventable. The current pay-and-chase model has always been a rearview-mirror look at what happened, followed by a scramble to correct it after the fact.  

That model has reached its limits. The complexity of the payment process frustrates all stakeholders, each with their own view on what needs fixing. As new procedures, drugs, and therapies proliferate, even more stress is added. Health plans face growing pressure from patients, who are increasingly vocal about their experience and their expectations.  

And, of course, there is the massive waste that serves no one. Healthcare accounts for nearly 20% of U.S. GDP. And by most credible estimates, roughly a quarter of that spending is pure waste. Every dollar of waste prevented can be redirected toward patient care.

Healthcare decision intelligence, the application of AI-driven pattern recognition and dynamic prioritization, combined with human oversight and grounded in explainable, auditable logic, offers a real path out of the rearview-mirror model. More than just incrementally better claims processing, it represents a fundamental shift in where accuracy begins, setting the stage for a post-pay correction to a pre-pay prevention model, ultimately moving to real-time decisioning at the point of care.  

Why First-Pass Accuracy Matters

To be sure, there are challenges to overcome and changes to occur, but when keeping the patient at the center of the episode of care, a new model may provide better outcomes. A first-pass approach enables this new model.  

Rather than processing a claim, issuing payment, and then chasing down errors through audits and appeals, the logic of first pass is to get it right the first time. Once care is received and a claim is generated, it is evaluated for accuracy before a provider receives payment. Errors are flagged and fixed, so the payment that goes out the door is accurate from the start.

Think about how your credit card works. If something looks irregular, you get a fraud alert before the charge clears: Is this you? Verification is low friction, nearly instantaneous, and far cheaper than disputing a fraudulent charge after the fact. This is the model healthcare payments should aspire to. The earlier an inaccuracy is caught, the faster and easier it is to resolve, the lower the total cost of care, the less abrasion between payers and providers, and most importantly, the better the patient experience.

The objective is straightforward: The pursuit is a clean claim. Ensure payment accuracy using healthcare decision intelligence on the first pass rather than relying on downstream correction.

Signal Over Noise: Why AI Changes the Equation

Health plans process billions of claims annually. Payment decisions involve thousands of dynamic coding combinations, rapidly changing policies, and fragmented data sources. The challenge for health plans is to identify meaningful patterns sufficiently quickly to support accurate decisions before payment occurs.

Distilling signals from noise to make an informed decision can be like looking for a needle in a haystack.

AI changes that equation, but only when it is grounded in clinical validation and accountable decision-making. Clinically validated decision intelligence can help health plans identify meaningful patterns across payment, policy, coding, clinical, and workflow information. That intelligence gives plans earlier visibility into where errors are likely to occur, which decisions require attention, and how to act before inaccuracies create downstream cost and friction.

But AI is a tool, not a decision-maker. A human must always be the one to throw the spear. Healthcare has a high cost of failure; therefore, AI is used to augment a human decision in an ethical, responsible, and governed manner. You would never want a decision this important and sensitive to be made solely by a machine or software. The stakes are too high for anything less than human accountability at the decision point.

The Evolution of Post-Pay Correction

As intelligence moves upstream, the payment process will ultimately evolve to include provider enablement and decision-making at the point of care. The potential trajectory:

Stage 1: Pre-pay prevention

We see the industry heading from post-pay to pre-pay, where claims are evaluated using HDI, after care is delivered but before payment is disbursed, proactively removing waste from the healthcare system.

An AI-driven healthcare payment platform can intercept and correct billing errors and inaccuracies before health insurance claims are paid.  

Stage 2: Provider enablement

Now, we can move that intelligence further upstream to the provider. When we roll out a new payment rule and look at where the errors are clustering, we often find that the root cause isn't fraud; it's confusion. A coding convention that providers haven't fully internalized. A policy change not clearly communicated. Nearly all of the actors in this industry are trying to do the right thing; they just don't always have the right information to do it. AI can surface those patterns and translate them into educational feedback that reduces errors at the source, before enforcement is ever necessary.  

Stage 3: The point-of-care decision intelligence‍

As organizations gain confidence in pre-pay decisioning and provider feedback loops, the next logical step is bringing that intelligence directly into clinical workflows. Rather than identifying issues after care has been delivered, payment and reimbursement considerations can be surfaced while care decisions are being made.

A physician selecting among clinically appropriate treatment options could receive visibility into reimbursement implications, documentation requirements, or patient cost exposure before the office visit ends. Ultimately, this gives providers access to holistic decision intelligence to determine the best pathway for each patient.  

Explainability, Trust, and the Non-Negotiables

Governance, compliance, and explainability are essential to implementation. In healthcare payments, claims are processed in milliseconds, and the rules, laws, and requirements are comprehensive. Everything must be traceable and auditable. CMS compliance, regulatory requirements, patient safety, none of these have any tolerance for a black box that produces a result but can't explain its reasoning.

This is why explainability isn't optional; it is foundational. Every finding, edit, and flag must include a clear and transparent rationale grounded in policy, medical codes, and documentation. Something a human reviewer can evaluate, question, challenge, and stand behind. The underlying logic must be able to withstand examination to be trusted. And in healthcare, trust isn't just a nice-to-have.

Ensuring transparency and traceability can also help calm concerns that the use of AI in payment systems will be deployed to deny more claims. The win is not in denying claims. The win across the care continuum is getting to “yes” faster and more accurately on the first pass. Denials generate appeals. Appeals generate administrative costs for everyone: payers, providers, and patients. The fear is understandable, but it confuses the tool with the intent.

The healthcare economy cannot afford to keep fixing errors after the fact. Healthcare decision intelligence enables us to prevent them before they exist. The infrastructure to deploy it is ready. What's required now is the willingness to make the move upstream, earlier in the workflow, toward a model that gets it right the first time. That's not a technology upgrade. It's a redefinition of what payment accuracy means, and what healthcare's administrative backbone owes to the patients it ultimately exists to serve.