“The risk is that the AI becomes a high-speed engine for denying care.”
That is the only part of this entire story that actually matters. While the government tries to frame the pilot program as a way to reduce administrative friction, anyone who has spent five minutes dealing with a claims adjuster knows that “friction” is often the only thing keeping a patient from being completely ghosted by their provider. Replacing a slow, grumpy human with a fast, polite algorithm doesn’t fix the underlying incentive structure; it just accelerates the rate at which you can be told no.
The fundamental problem here is that insurance companies are not in the business of providing care; they are in the business of managing risk and minimizing payouts. In a manual system, a human reviewer might actually read a doctor’s note and realize a procedure is necessary, even if it doesn’t fit a perfect checkbox. An AI, however, is only as good as the tokens it’s optimized for. If the objective function is “reduce spend while maintaining minimum legal compliance,” the model will find every possible loophole to justify a denial. Do we really believe the goal here is patient wellness? (Probably not, unless the AI is specifically tuned for altruism, which is not a standard feature in corporate software).
When a human denies a claim, there is usually a paper trail—a specific policy line or a clinical guideline cited. When an LLM does it, we enter the era of the probabilistic denial. As noted in the Ars Technica report, the lack of transparency in these decisions is a ticking time bomb. It is essentially the Zillow iBuying disaster all over again: trusting a model to price a complex, real-world asset—or in this case, a human life—without realizing the model is just hallucinating a pattern based on bad data. It is like replacing a trial judge with a slot machine that is weighted heavily toward the house.
Then there is the real-world friction of the appeal process. Fighting a human is annoying, but fighting a model that has been integrated into a corporate workflow is a nightmare. The latency isn’t in the compute—it’s in the bureaucracy. If the AI denies a claim in three seconds, but the human appeal process still takes three weeks, the “efficiency” only benefits the payer. This creates a psychological war of attrition. By Q1 2027, we will see the first major class-action lawsuit where a patient’s legal team successfully argues that an LLM’s hallucination—rather than a medical guideline—was the primary driver of a denied life-saving treatment.
The irony is that we’re likely to see a “prompt engineering” arms race in healthcare. Doctors will stop writing clinical notes for other doctors and start writing them for the insurance AI, using specific keywords and structures designed to trick the model into a “Yes” output. We’ll end up with a system where the quality of your medical care depends on how well your physician can optimize their prose for a corporate bot. It turns the act of healing into a game of SEO, where the goal is to rank high enough in the “approved” category to get a scan. Or maybe not—maybe the AI will be trained on these “optimized” notes and simply learn to ignore the keywords.
This is just a faster way to say no.
