The Waiting Room Has an Algorithm Now
Prior authorization has long been one of the most friction-heavy processes in American healthcare – a bureaucratic checkpoint where insurers demand proof that a prescribed treatment is medically necessary before agreeing to pay for it. Patients and physicians have spent years documenting the damage: delayed care, abandoned treatments, and appeals stacked on top of appeals. Now, artificial intelligence is being positioned as the fix. Whether it actually functions that way is an open and increasingly contentious question.
The pitch is straightforward enough. AI systems can process large volumes of structured data fast, which means they could, in theory, sort clearly approvable claims from genuinely ambiguous ones without the multi-day lag that currently defines the process. Speed the approvals, reduce the backlogs, get patients into treatment sooner. That is the optimistic version. A growing number of physicians are not buying it.
What Prior Authorization Actually Does – and What It Costs
When used carefully, prior authorization works as a cost-containment mechanism. Insurers use it to verify that a patient is receiving the most clinically appropriate treatment given their condition and available alternatives, rather than defaulting to newer, more expensive options when cheaper ones exist. The logic has a basis in medical economics. Unchecked prescribing and procedure volume can drive spending upward without proportional improvements in outcomes. A review layer, in principle, addresses that.
The operational reality tends to diverge sharply from that principle. Physicians have documented cases where prior authorization delays cause patients to simply give up on recommended treatments – not because the treatment was inappropriate, but because the wait became untenable. The approval process can stretch across days or weeks, during which a patient’s condition may change or their willingness to continue pursuing coverage may erode entirely. Denials trigger appeal rights, but appeals take time too, and not every patient has the capacity to navigate that process.
The friction is not incidental. It is structural. Insurers process enormous volumes of authorization requests across thousands of procedure codes, drug tiers, and plan designs. That scale creates the conditions where an AI-assisted system starts to look attractive – not because AI is inherently more accurate than human reviewers, but because human reviewers working at scale are already making decisions quickly and under pressure. The question is whether automating that pressure produces better outcomes or simply faster ones.
Where Physician Concerns Are Landing
A 2025 American Medical Association survey of physicians produced a specific and pointed finding: 61 percent of doctors expressed concern that AI tools used in prior authorization will increase denials of treatments they consider medically necessary. That is not a marginal worry from a skeptical fringe. It is the majority position among surveyed physicians, and it reflects a concern that pattern-matching at scale will penalize clinical nuance.
AI systems trained on historical claims data learn what has been approved before. That creates an implicit standard derived from past insurer behavior – which is not the same as current clinical evidence or the specifics of an individual patient’s condition. A physician recommending a treatment that departs from historical norms, even for documented clinical reasons, may find that an AI reviewer flags the request as anomalous. The mechanism that is supposed to reduce wrongful denials may, in practice, generate more of them.
The Gadget Layer Underneath the Policy Fight
It is worth being precise about what AI in prior authorization actually means at a technical level. These are not general-purpose language models making holistic clinical judgments. They are typically machine learning systems trained on structured claims data – procedure codes, diagnosis codes, formulary tiers, historical approvals and denials – tasked with matching incoming requests against learned patterns. The output is a recommendation or an automated decision, depending on how much human review remains in the loop.
That distinction matters because the failure modes are different from what people imagine when they hear “AI.” Hallucination in the large language model sense is not really the risk here. The risk is overfit – a model that learned what past insurers approved and now reproduces that logic mechanically, without the capacity to weigh the kind of individual clinical context that a physician spent years training to evaluate. The tool is fast and consistent. It is not necessarily right.
Insurers who deploy these systems face a regulatory environment that is still catching up. Several states have moved to require human review on AI-generated denials, but there is no uniform federal standard governing how much automation is permissible in coverage decisions. That gap means patients in different states face materially different exposure to fully automated denials, depending on where they live and which insurer covers them.
Physicians who already spend significant time managing prior authorization workflows – documenting clinical necessity, submitting requests, handling appeals – are not necessarily opposed to a system that moves faster. What the AMA survey captures is a specific worry that speed will be decoupled from accuracy, and that the cases that fall outside the historical norm will be the ones that get denied wrongly and quietly. Appeals take time. Not every patient appeals. A denial that never gets challenged looks, in the data, like a correct decision.
The authorization request that gets flagged by an algorithm at 2 a.m., reviewed by no human, and denied before a physician even knows it happened – that is the scenario that 61 percent of surveyed doctors say they are afraid is already arriving.
