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AI in insurance coverage decisions: Three practical lessons from Estate of Lokken v. UnitedHealth Group

7/28/26

By: Cory Chipman and Katie Graham

In Estate of Gene B. Lokken v. UnitedHealth Group, Inc., 2026 WL 658883 (D. Minn. 2026), the United States District Court for the District of Minnesota considered the scope of discovery when plaintiffs challenged an insurer’s use of artificial intelligence (AI) in Medicare Advantage coverage decisions. The plaintiffs alleged that UnitedHealth’s use of an AI tool, nH Predict, was inconsistent with plan materials stating that coverage decisions would be made by clinicians. Because those allegations focused on whether the insurer followed its contractual promises, the court allowed discovery into policies, procedures, governance, regulatory oversight, and how the AI tool was used in practice.

At the same time, the court did not give plaintiffs unlimited access. It limited requests for certain internal investigations and financial or profitability information that were not tied closely enough to the contract claims. The court also denied requests for the AI system’s source code, underlying data, and embedded medical guidelines, recognizing the need to protect proprietary technical information. For insurers, Lokken offers three practical lessons:

First, using AI in claims handling may open the door to broad discovery about how the tool is implemented, supervised, and used in practice. In Lokken, the court permitted discovery into governance structures, training materials, oversight mechanisms, and vendor relationships because the plaintiffs claimed that AI replaced individualized clinical judgment. Insurers should be prepared to show that AI is used as a support tool, rather than a substitute for required human review. Clear documentation, user training, and oversight procedures can help demonstrate how AI recommendations are generated, reviewed, and acted on.

Second, policy language regarding how coverage decisions will be made is critical. The contract language was central to the court’s analysis, which turned on whether the insurer’s actual process matched its representations about how coverage decisions would be made. If policy language, evidence-of-coverage documents, or member communications promise clinician involvement, insurers should ensure that their workflows support that promise. Any gap between what the documents say and what happens in practice can increase litigation and discovery risk.

Third, proprietary AI materials may receive protection, but operational and governance-level information likely will not. Lokken suggests that courts may protect source code and technical data while still requiring production of documents showing how AI affects claims handling, oversight, and decision-making.

Lokken underscores that courts may scrutinize insurers’ use of AI in claims handling and permit broad discovery into operational practices where contractual compliance is at issue. Insurers can reduce litigation risk by aligning policy language with real-world workflows, maintaining robust oversight records, and documenting the role of human review in AI-assisted decisions.

For any questions or further clarification, please contact Cory Chipman at cory.chipman@fmglaw.com, Katie Graham at katie.graham@fmglaw.com or your local FMG attorney.

Information conveyed herein should not be construed as legal advice or represent any specific or binding policy or procedure of any organization. Information provided is for educational purposes only. These materials are written in a general format and not intended to be advice applicable to any specific circumstance. Legal opinions may vary when based on subtle factual distinctions. All rights reserved. No part of this presentation may be reproduced, published or posted without the written permission of Freeman Mathis & Gary, LLP.