The U.S. government is testing an AI system to streamline prior authorization, the process where insurers require approval before covering certain medical procedures. The pilot program aims to reduce delays that currently frustrate patients and doctors who spend thousands of hours annually filing paperwork and fighting denials.

Prior authorization exists ostensibly to prevent unnecessary care and control costs. In practice, it often blocks or delays legitimate treatments. A 2022 American Medical Association survey found 91 percent of physicians report prior authorization delays harm patient care, with 28 percent citing serious adverse events. Doctors lose roughly 14 hours per week per practice navigating these requirements.

AI offers potential efficiency gains. Automated systems could review claims instantly against coverage criteria, eliminating manual bottlenecks. The government's pilot tests whether algorithms can make faster, consistent decisions without the human delays that currently plague the system.

The risks run deep, however. AI systems inherit biases from training data and can perpetuate existing disparities in healthcare access. If an algorithm learns from historical denial patterns, it may replicate those same discriminatory decisions at scale. Prior authorization denials already disproportionately affect Black and Latino patients and those with chronic conditions. Automating a flawed process amplifies harm.

Additionally, AI systems can fail in ways humans catch. A radiologist may recognize a complex case requiring exception approval. An algorithm trained on simpler claims might auto-deny without escalating. Insurance companies have financial incentives to deny claims, raising concerns they'll optimize AI systems for cost-cutting rather than patient welfare.

Accountability becomes murky. When an AI system denies coverage, who bears responsibility? The vendor? The insurer? The regulator? Current insurance denials already lack transparency. Adding machine learning creates another opacity layer.

The core problem persists: prior authorization shouldn't exist in its current form. Whether a human or algorithm reviews claims