Court Orders Broad Discovery in UnitedHealth AI Suit
A federal magistrate judge in Minnesota ordered UnitedHealth Group on March 9, 2026, to produce a wide range of documents in a class-action lawsuit over its use of the nH Predict AI tool to deny post-acute care to Medicare Advantage members. The order granted or partially granted six of seven discovery requests, giving plaintiffs access to material that could show how the tool was developed, deployed, and overseen.
The case was originally filed in 2023 by the families of two deceased Medicare Advantage members. The plaintiffs allege that UnitedHealthcare, through its Optum subsidiary naviHealth, used nH Predict to override physician judgments about medically necessary skilled nursing facility care. Optum has disputed the claims, stating that nH Predict is a care-support tool and that medical necessity decisions are made by qualified physicians following CMS guidance, not by AI.
Under the order, UnitedHealth must produce documents dating back to January 2017 covering policies and procedures for post-acute care claims, internal analyses of nH Predict, records related to the naviHealth acquisition and post-acute care cost savings, government investigations into AI claims adjudication, performance evaluations for post-acute care coordinators and medical directors, internal AI review board records, and contact information for care coordinators involved in coverage denials for 300 proposed class members. The court rejected UnitedHealth’s argument that documents predating the July 2019 deployment of nH Predict were irrelevant, noting that pre-2019 records could serve as circumstantial evidence.
The ruling matters for insurers beyond the specific allegations. It shows that courts are willing to order deep discovery into AI systems, including acquisition rationale, internal governance boards, and compensation incentives. The denial of some requests, such as nH Predict’s source code and underlying medical guidelines, suggests there are limits to what plaintiffs can demand, but the breadth of what was granted is notable. The court also found that plaintiffs are entitled to discover how the tool works, its development goals, and its performance across time.
For carriers, the case is a reminder that AI tools used in utilization management or claims decisions must be documented in ways that can withstand litigation scrutiny. That includes evidence of clinical peer review, override mechanisms, and ongoing monitoring of denial rates and outcomes. It also means that internal communications about cost savings, productivity, and AI goals may become discoverable in future litigation. Carriers should review document retention policies and litigation hold procedures to ensure they can preserve and produce relevant materials without being caught by surprise.
The order also underscores the importance of the human-in-the-loop design. If a tool is described to regulators or members as supporting clinical decisions, the discovery process will test whether the design, training, and incentives actually support that claim. Carriers should be able to show that the AI is used to augment, not replace, clinical judgment, and that the final decision-maker has the authority and information to disagree with the AI.
The order arrives as several other AI denial cases move through courts and state legislatures tighten rules on AI use in health insurance decisions. The overlap between litigation and regulation means that carriers cannot rely on one defense alone; they must be able to demonstrate both regulatory compliance and fair process.
For more on how carriers are building governance for AI in health insurance, see our guide to AI in health insurance.