Dr Debby Day is a Cigna whistleblower. Dr Day was a Cigna medical director, charged with reviewing denied cases, a job she held for 20 years. In 2022, she was forced out by Cigna. Writing for @ProPublica and *The Capitol Forum*, Patrick Rucker and David Armstrong tell her story, revealing the true "equilibrium" that Cigna has found:

https://www.propublica.org/article/cigna-medical-director-doctor-patient-preapproval-denials-insurance

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Dr Day took her job seriously. Early in her career, she discovered a pattern of claims from doctors for an expensive therapy called intravenous immunoglobulin in cases where this made no medical sense. Dr Day reviewed the scientific literature on IVIG and developed a Cigna-wide policy for its use that saved the company millions of dollars.

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This is how it's supposed to work: insurers (whether private or public) should permit all the medically necessary interventions and deny interventions that aren't supported by evidence, and they should determine the difference through internal reviewers who are treated as independent experts.

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But as the competitive landscape for US healthcare dwindled - and as Cigna bought out more parts of its supply chain and merged with more of its major rivals - the company became uniquely focused on *denying* claims, irrespective of their medical merit.

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In Dr Day's story, the turning point came when Cinga outsourced pre-approvals to registered nurses in the Philippines. Legally, a nurse can *approve* a claim, but only an MD can *deny* a claim. So Dr Day and her colleagues would have to sign off when a nurse deemed a procedure, therapy or drug to be medically unnecessary.

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This is a complex determination to make, even under ideal circumstances, but Cigna's Filipino outsource partners were far from ideal. Dr Day found that nurses were "sloppy" - they'd confuse a mother with her newborn baby and deny care on that grounds, or confuse an injured hip with an injured neck and deny permission for an ultrasound.

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Dr Day reviewed a claim for a test that was denied because STI tests weren't "medically necessary" - but the patient's doctor had applied for a test to diagnose a toenail fungus, not an STI.

Even if the nurses' evaluations had been careful, Dr Day wanted to conduct her own, thorough investigation before overriding another doctor's judgment about the care that doctor's patient warranted.

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When a nurse recommended denying care "for a cancer patient or a sick baby," Dr Day would research medical guidelines, read studies and review the patient's record before signing off on the recommendation.

This was how the claims denial process is *said* to work, but it's not how it was *supposed* to work.

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Dr Day was markedly slower than her peers, who would "click and close" claims by pasting the nurses' own rationale for denying the claim into the relevant form, acting as a rubber-stamp rather than a skilled reviewer.

Dr Day knew she was slower than her peers. Cigna made sure of that, producing a "productivity dashboard" that scored doctors based on "handle time," which Cigna describes as the average time its doctors spend on different kinds of claims.

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But Dr Day and other Cigna sources say that this was a *maximum*, not an average - a way of disciplining doctors.

These were *not* long times. If a doctor asked Cigna not to discharge their patient from hospital care and a nurse denied that claim, the doctor reviewing that claim was supposed to spend not more than 4.5 minutes on their review. Other timelines were even more aggressive: many denials of prescription drugs were meant to be resolved in fever than *two minutes*.

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Cigna told *Propublica* and *The Capitol Forum* that its productivity scores weren't based on a simple calculation about whether its MD reviewers were hitting these brutal processing time targets, describing the scores as a proprietary mix of factors that reflected a nuanced view of care.

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But when *Propublica* and *The Capitol Forum* created a crude algorithm to generate scores by comparing a doctor's performance relative to the company's targets, they found the results fit very neatly into the actual scores that Cigna assigned to its docs:

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> The newsrooms’ formula accurately reproduced the scores of 87% of the Cigna doctors listed; the scores of all but one of the rest fell within 1 to 2 percentage points of the number generated by this formula. When asked about this formula, Cigna said it may be inaccurate but didn’t elaborate.

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As Dr Day slipped lower on the productivity chart, her bosses pressured her bring her score up (Day recorded her phone calls and saved her emails, and the reporters verified them). Among other things, Dr Day's boss made it clear that her annual bonus and stock options were contingent on her making quota.

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Cigna denies all of this. They smeared Dr Day as a "disgruntled former employee" (as though that has any bearing on the truthfulness of her account), and declined to explain the discrepancies between Dr Day's accusations and Cigna's bland denials.

This isn't new for Cigna. Last year, *Propublica* and *Capitol Forum* revealed the existence of an algorithmic claims denial system that allowed its doctors to bulk-deny claims in as little as *1.2 seconds*:

https://www.propublica.org/article/cigna-pxdx-medical-health-insurance-rejection-claims

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Cigna insisted that this was a mischaracterization, saying the system existed to speed up the *approval* of claims, despite the first-hand accounts of Cigna's own doctors and the doctors whose care recommendations were blocked by the system. One Cigna doctor used this system to "review" and deny *60,000* claims in *one month*.

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Beyond serving as an indictment of the US for-profit health industry, and of Cigna's business practices, this is also a cautionary tale about the idea that critical AI applications can be resolved with "humans in the loop."

AI pitchmen claim that even unreliable AI can be fixed by adding a "human in the loop" that reviews the AI's judgments:

https://pluralistic.net/2024/04/23/maximal-plausibility/#reverse-centaurs

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In this world, the AI is an assistant to the human. For example, a radiologist might have an AI double-check their assessments of chest X-rays, and revisit those X-rays where the AI's assessment didn't match their own. This robot-assisted-human configuration is called a "centaur."

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In reality, "human in the loop" is almost always a *reverse-centaur*. If the hospital buys an AI, fires half its radiologists and orders the remainder to review the AI's superhuman assessments of chest X-rays, that's not an AI assisted radiologist, that's a radiologist-assisted AI. Accuracy goes down, but so do costs. That's the bet that AI investors are making.

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