Healthcare AI Won’t Fix Reimbursement by Writing Appeals Faster

The future of AI in procedural healthcare isn't about processing claims faster. It's about connecting clinical evidence, patient access, and reimbursement so physicians can deliver appropriate care sustainably.

The Business of Healthcare Is Becoming Disconnected From the Delivery of Care

Every ENT surgeon recognizes the scenario.

A patient spends months navigating referrals, diagnostic testing, medical therapy, and specialist evaluations. Eventually, the surgeon determines that a procedure is clinically appropriate.

The decision is supported by the patient's history, examination, diagnostic findings, and response to prior treatment.

Then the administrative process begins.

The insurer requests additional documentation. Staff search through disconnected medical records, reconstruct the clinical history, and prepare a prior authorization submission. A denial may follow, triggering another cycle of appeals and correspondence.

Even when the procedure is eventually performed, reimbursement may be delayed, reduced, or challenged months later. The underlying problem is frequently not the absence of clinical evidence.

It is the absence of an infrastructure that connects clinical evidence to the administrative and financial decisions determining whether care can be delivered.

Healthcare has developed increasingly sophisticated systems for documenting encounters, submitting claims, and processing payments. Yet these systems often operate independently of the longitudinal clinical information that establishes why treatment was appropriate in the first place.

That disconnect creates consequences extending far beyond administrative inconvenience.

For physicians, it increases operating costs, introduces financial uncertainty, and consumes resources that should support patient care. For patients, it creates delays and sometimes prevents recommended treatment altogether. For medical device manufacturers, it introduces a frequently overlooked barrier between clinical demand and actual procedural utilization.

The American Medical Association's 2025 prior authorization survey illustrates the magnitude of the problem. Among surveyed physicians, 95% reported that prior authorization requirements delayed necessary care, while 79% reported that patients at least sometimes abandoned recommended treatment because of these requirements.

These are physician-reported findings across specialties, but the underlying implications are particularly relevant to procedural medicine.

A patient who never reaches appropriate treatment represents more than a lost claim. It represents a failure of access, an economic loss to the practice, and potentially a missed opportunity to deliver the clinical benefits of a medical technology.

The objective should not simply be to make reimbursement administration more efficient. It should be to prevent administrative dysfunction from determining whether appropriate care occurs.

Connect Clinical Evidence to the Economics of Delivering Care

Much of today's healthcare AI development focuses on making existing administrative tasks faster.

AI can generate clinical notes, prepare appeal letters, identify coding deficiencies, classify denials, and automate communications. These capabilities can reduce labor. But automating fragmented workflows does not necessarily eliminate the fragmentation.

A more consequential opportunity is to connect information across the entire patient journey—from initial referral through clinical evaluation, treatment, authorization, and final payment.

Consider three questions that every procedural practice must answer.

1. Was the treatment clinically appropriate and adequately supported?

The supporting evidence often originates long before authorization is requested. Previous medical management, imaging, symptom history, examination findings, and documented treatment responses may be distributed across multiple systems and encounters.

An intelligent infrastructure should preserve this information with its source, date, and clinical context, allowing relevant evidence to be retrieved when needed.

The purpose is not to manufacture medical necessity or alter clinical judgment to satisfy insurance criteria. It is to ensure that accurate documentation of appropriate care remains accessible throughout the process.

2. Was the service reimbursed correctly?

Once a procedure is performed, the problem changes. The practice must connect the service delivered with the claim, applicable payer contract, reimbursement policies, remittance information, and subsequent adjustments.

Without these connections, physicians may know what they collected without knowing whether they received the payment contractually owed.

AI offers an opportunity to identify discrepancies, recurring denial patterns, and reimbursement problems that traditional billing reports may fail to reveal.

3. Was the reimbursement economically sustainable?

Correct payment does not necessarily mean adequate payment. A practice may receive exactly what its contract specifies while still being reimbursed below the actual cost of delivering care.

Understanding that distinction requires connecting reimbursement with physician time, staffing, equipment, supplies, overhead, and other procedural expenses. This is where AI can begin moving beyond revenue cycle management into meaningful practice intelligence.

These three questions are related, but they require different information and analytical capabilities. Medical necessity, payment accuracy, and economic sustainability are not interchangeable problems.

The opportunity is to create an infrastructure capable of evaluating all three while maintaining the clinical context connecting them. That approach also addresses a fundamental imbalance in healthcare.

Large insurers possess extensive claims databases, contract information, utilization histories, and increasingly sophisticated analytical capabilities. Individual physician practices often operate with fragmented financial information and limited insight into their contractual performance.

AI has the potential to reduce that information asymmetry—not by generating more appeals, but by giving physicians a clearer understanding of the evidence, policies, payments, and costs affecting their practices.

Importantly, better information cannot guarantee authorization or reimbursement. But it can help identify problems earlier, support appropriate challenges, and improve decisions about practice operations and contracting.

Build AI Infrastructure Around the Problems Surgeons Actually Experience

At Nemedic, we approach healthcare AI from a different starting point. As a practicing ENT surgeon, I understand that the central problem is not simply the time spent completing administrative tasks. It is that disconnected administrative systems interfere with patient access, physician productivity, and the financial sustainability of procedural care.

Our work on Blue, our AI platform for procedural healthcare, begins with those problems rather than the individual workflows traditionally used to manage them.

We are developing AI-first infrastructure to help connect referral intake, patient engagement, surgical qualification, scheduling, and the clinical information needed to support appropriate treatment.

The longer-term opportunity extends into authorization preparation, reimbursement analysis, and contract intelligence. These require additional integrations, validation, and specialized capabilities; they should not be confused with functions already deployed.

The underlying principle remains consistent:

Clinical information should retain its value throughout the entire patient journey—not disappear into disconnected records after each encounter.

For ENT surgeons, that means moving toward systems that do more than reduce documentation time or automate front-desk work. The goal is better access for appropriate patients, less repetitive administrative effort, improved procedural capacity, and greater visibility into the economics of clinical practice.

For medical device executives, the implications are equally significant.

Traditional commercial strategies emphasize surgeon adoption, product differentiation, reimbursement coverage, and procedural training. But having an effective technology, an appropriately trained physician, and a patient who may benefit does not guarantee that a procedure will occur.

Patients must successfully navigate the operational and financial infrastructure between clinical need and treatment. Understanding where that process breaks down—and distinguishing patients who are not clinically appropriate from those facing avoidable administrative obstacles—could become an important component of market access and commercial strategy.

The opportunity is not simply to increase procedure volume. It is to help more appropriate patients reach treatment while allowing physicians to deliver that care sustainably.

The Future Is Not Faster Denials and Faster Appeals

Healthcare is approaching a consequential decision about how artificial intelligence will be implemented.

We can continue building increasingly sophisticated tools for opposing sides of the reimbursement process: payer AI identifying reasons to deny care and physician AI generating documentation and appeals in response.

Or we can develop infrastructure that makes relevant clinical evidence available when needed, improves transparency into payment decisions, and identifies administrative and economic problems before they become barriers to treatment.

The second approach requires a much broader view of healthcare AI.

For surgeons, the measure of success should not be how many administrative tasks a system automates. It should be whether appropriate patients receive timely care and whether the practice becomes more clinically and economically effective.

For medical device manufacturers, it should include understanding how the infrastructure surrounding care influences appropriate utilization, access, and adoption.

The next generation of healthcare AI will be defined less by how efficiently it performs administrative work and more by how effectively it connects clinical decisions to the delivery and economics of care.

That is the problem we are working to solve at Nemedic. And it is why the future of healthcare AI must be about far more than revenue cycle automation.

Fire up!

Brad Bichey, MD, is a practicing otolaryngologist and Founder and CEO of Nemedic, Inc., developer of Blue (useBlue.ai), an AI platform focused on improving patient access, practice operations, and the delivery of procedural healthcare.