AI is already deciding whether or not people receive medical care

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Health insurers are deploying AI to deny claims — and putting people’s health at risk.

The AI freakout of the past few weeks has centered on nothing less than the risk of human extinction — on the possibility that artificial intelligence could wipe us all out. But the reality is, AI programs are already having a direct and sinister effect on people’s physical health: UnitedHealth Group, the largest health insurer in the US, has been accused of using AI programs to deny payments to people, despite their doctors saying the care was necessary, and is facing a class-action lawsuit.

Humana and Cigna are facing their own litigation over similar allegations. Meanwhile, Medicare has launched its own AI tool that will help the government determine whether to approve prior authorizations, and doctors are sending the federal government reports of patients at their offices who are in tears because they can’t get the medicine to manage their chronic pain.

The claims in the legal complaints are harrowing: Families are desperate for an elderly loved one who recently experienced a serious fall to be allowed to stay in a nursing home. Doctors are flummoxed when a patient who may need months of rehab is cut off by an algorithm after a couple of weeks.

While the current conversation may be fixated on something more Hollywood, private insurers and the Trump administration are already deploying AI programs to vet people’s health insurance claims — and the evidence is growing that these programs are not always doing a good job and are instead putting people’s health at risk. Until these lawsuits are settled or policymakers can figure out a way to regulate how insurers use this new technology, it will be up to each of us to make sure that an AI program isn’t being used to cheat us out of insurance benefits that we’re entitled to.

“The type of care that clinicians deliver is very sensitive to the patient’s unique circumstances. That may not really show up as a number. There’s not a numeric value that tells you the completeness and connection of a patient’s social network,” said Mika Hamer, a health services researcher at the University of Maryland. “There really are a lot of factors that really can’t be quantified and so aren’t really suitable to go into these models. If we start to rely exclusively on the models, I think we run the risk of losing that human touch.”

Insurers and patients fighting over medical claims is nothing new: Policyholders want the most coverage they can get and payers have a business incentive to pay as little as they can. Health insurers have also used algorithms for decades: Plug in somebody’s age, their weight, whether or not they smoke, and one of the old-fashioned algorithms could spit out a rough estimate of how much medical care they would need before they died. Simple — and comparatively transparent.

When you submitted a claim or sought prior authorization, there was supposed to be a human doctor who could review all of the relevant information on behalf of the insurer and make the final decision, in theory with the patient’s best interests in mind. It didn’t always work that way in practice, of course, but that was the idea.

What’s different right now, according to Daniel Schwarcz, a law professor at the University of Minnesota who has looked at health insurers’ AI use, is that these newer programs can make very specific predictions about someone’s future health based on the information that’s fed into them, and those predictions are being used in the process of approving or denying medical claims. And exactly what went into making those predictions — and whether it is the AI or the human who ultimately holds the final authority — is often opaque from the outside.

“The reason we want human review is we want human substantive judgment. We want AI to be supporting that,” Schwarcz said. “But the difficulty is distinguishing between AI supporting meaningful human processing and AI basically replacing the human with the human just passively defaulting to the AI judgment. It’s just incredibly hard to distinguish between those two in practice, particularly in rules or regulations.”