Class action alleges UnitedHealth used naviHealth's nH Predict algorithm, with a 90% error rate, to deny Medicare Advantage post-acute care
What happenedOn November 14, 2023, the families of two deceased Medicare Advantage members, including Gene B. Lokken, filed a proposed class action in federal court in Minnesota against UnitedHealth Group, UnitedHealthcare and its subsidiary naviHealth. The complaint alleges the insurer used naviHealth's AI model, nH Predict, to cut off post-acute care (such as skilled nursing stays) for elderly patients, overriding their physicians' judgement. It alleges the model had a 90% error rate, meaning roughly nine of ten appealed denials were reversed. It also alleges the companies kept using it because only about 0.2% of policyholders ever appeal. Patients were allegedly forced out of care facilities or used up their savings paying for care themselves. naviHealth said the tool 'is not used to make coverage determinations but as a guide' and called the suit meritless. On February 13, 2025, U.S. District Judge John Tunheim dismissed five of the seven claims. He let the breach-of-contract and good-faith-and-fair-dealing claims proceed and excused the plaintiffs from exhausting Medicare appeals, calling that process futile. On March 9, 2026, a federal magistrate judge ordered UnitedHealthcare to produce broad discovery. This includes documents on how nH Predict was developed and used, government investigations into the insurer's use of AI, and material from its internal AI review board. The judge also refused its request to stage discovery. The case remained in discovery later in 2026.
Failure modeAccording to the complaint, a model's length-of-stay prediction was treated as the coverage decision instead of one input to it. This let it override treating physicians. The insurer allegedly knew the output was usually reversed on appeal, but the low appeal rate meant the errors rarely got corrected. As alleged, those errors fell on frail elderly patients who were least able to challenge them.
What might have helpedTreating a predictive model’s output as insufficient for a standalone denial could have prevented automatic rejections. Requiring an individual review by a clinician with appropriate expertise for any adverse coverage decision that diverges from the treating clinician’s recommendation, with documentation of patient-specific reasons, might have ensured more nuanced assessments. Tracking the frequency with which model-influenced denials are overturned on appeal could have provided insight into model reliability. Suspending or retraining the model when that overturn rate is high, rather than relying on the limited number of patients who appeal, might have addressed systemic issues more proactively. Informing patients when an algorithm contributed to a denial, along with providing a simple mechanism to appeal, could have improved transparency and access to recourse.
Partly mitigated Since thenOn February 6, 2024, CMS issued an FAQ under its Contract Year 2024 Medicare Advantage rule. It says plans must base coverage decisions on the individual patient's circumstances, and that an algorithm that determines coverage from a larger data set instead of the patient's medical history, physician's recommendations or clinical notes is not compliant.
Still openThe FAQ is guidance on an existing rule and names no specific enforcement or penalties. It still lets plans use algorithms to assist decisions. No regulator finding or court order has required UnitedHealth or naviHealth to change nH Predict, and the class action is still in discovery.
LessonThe rule now says an algorithm cannot decide coverage on its own, but without enforcement the remaining safeguards are the insurer's own individual clinician review and its monitoring of appeal overturns.
Sources: aamc.org · reedsmith.com
Updates: 2026-09-30: The case is still in discovery. On March 9, 2026 a federal magistrate judge in Minnesota ordered UnitedHealthcare to produce wide-ranging documents on nH Predict and refused its request to split discovery into stages.