The Hook
Gene Lokken was 91 years old. He had fractured his leg and was in a rehabilitation facility. His insurer paid for 19 days of rehab, then issued a discharge authorization: he was ready to go home. His physical therapist's notes said his muscles were still partially paralyzed. The decision that he was ready had been made by a model called nH Predict.
The Question
When an AI model makes medical discharge decisions with a claimed error rate of roughly 90%, and only about 0.2% of affected policyholders ever appeal, what is the product actually doing?
The Paper Trail
nH Predict is a decision-support tool used by naviHealth, a UnitedHealth subsidiary. It does not examine patients. It compares the presenting condition against a database of approximately 6 million prior patients and returns a discharge date. The complaint in Estate of Gene B. Lokken et al. v. UnitedHealth Group, filed in the District of Minnesota in November 2023, alleges that the model's recommendations were wrong approximately 90% of the time when appealed — the overwhelming majority of denials were overturned on appeal.
The key structural fact alleged: only approximately 0.2% of policyholders appeal. If the error rate is approximately 90% but the appeal rate is approximately 0.2%, the overwhelming majority of erroneous denials are never challenged. The model's financial benefit depends on that gap. In 2026, a court ordered UnitedHealth to disclose how the tool works. UnitedHealth states that nH Predict is a guide, not the basis for denials.
The Synthesis
The business logic is in the numbers: if the appeal rate is low enough, a high error rate on denials is still profitable. The model is not the actor that created this arithmetic. The people who deployed it, set the denial thresholds, and chose not to raise the appeal rate by simplifying the process, are the actors. The AI is the tool through which the decision was implemented and the entity to which the denial was attributed when challenged.