Insurers Are Using AI to Deny Claims — Here's What Courts Are Saying About It | ICA

1.0 Introduction

 

Your claim came back denied within seconds. No phone call, no records request, nothing that looked like a person reading your file. If that felt less like a medical judgment and more like a machine saying no, you weren't imagining it.

 

Three federal class actions filed against major health insurers now allege exactly that — that predictive software, not individualized human review, decided whether patients kept their coverage. None of these cases has produced a final ruling on liability. But one of them just cleared a real hurdle: in March 2026, a federal court ordered one insurer to open up its AI system to scrutiny for the first time.

 

This guide explains what's actually happened in court, what hasn't, how to tell whether AI touched your own denial, and what to do next. For the broader picture, see our pillar guide, [Recognizing Bad Faith Insurance Practices](/bad-faith-insurance/).

 

2.0 Table Of Contents

 

1. [How Insurers Use AI to Process and Deny Claims](#1-how-insurers-use-ai-to-process-and-deny-claims)

2. [The Major Lawsuits: UnitedHealth, Cigna, and Humana](#2-the-major-lawsuits-unitedhealth-cigna-and-humana)

3. [Can an AI Denial Be Evidence of Bad Faith?](#3-can-an-ai-denial-be-evidence-of-bad-faith)

4. [Courts Allowing Discovery Into Insurer AI Systems](#4-courts-allowing-discovery-into-insurer-ai-systems)

5. [How to Tell If AI Was Involved in Your Denial](#5-how-to-tell-if-ai-was-involved-in-your-denial)

6. [Steps to Take If You Suspect an Automated Denial](#6-steps-to-take-if-you-suspect-an-automated-denial)

7. [Frequently Asked Question](#7-faq)

 

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3.0 How Insurers Use AI To Process And Deny Claims

 

Software touches nearly every claim an insurer processes. Most of that is unremarkable — routing, duplicate checks, fraud flags. The dispute that's ended up in federal court is narrower: whether software is making, or effectively making, the decision to deny coverage.

 

Two tools sit at the center of the current litigation. nH Predict, built by naviHealth (a UnitedHealth Group subsidiary later folded into Optum), projects how many days of skilled nursing or rehab care a Medicare Advantage patient "should" need, based on comparisons to similar past patients. Plaintiffs in three separate cases allege insurers used that projected number as the actual cutoff date for coverage — regardless of what the treating physician recommended. A naviHealth spokesperson has described the tool differently, telling [CBS News](https://www.cbsnews.com/news/unitedhealth-lawsuit-ai-deny-claims-medicare-advantage-health-insurance-denials/) it functions as <cite index="33-1">"a guide to help inform providers"</cite> rather than a coverage determination on its own.

 

Why This Matters to You: if your rehab or home-health coverage ended on a specific day that didn't match your doctor's recommendation, and nobody could explain why that day was chosen, that gap between "projected" and "medically necessary" is worth documenting.

 

Cigna's PxDx ("procedure-to-diagnosis") process works differently — it batch-matches procedure codes against diagnosis codes for review. [ProPublica's March 2023 investigation](https://www.propublica.org/article/cigna-pxdx-medical-health-insurance-rejection-claims), based on internal company documents and interviews with former Cigna medical directors, reported that Cigna doctors denied more than 300,000 requests over two months in 2022, spending an average of 1.2 seconds per case. One former physician described the process bluntly: <cite index="13-1">"We literally click and submit"</cite>. That's investigative reporting Cigna disputes, not a court finding — but it's the fact pattern that triggered the Kisting-Leung lawsuit discussed below.

 

Volume is where the numbers get hard to ignore. In 2024, Medicare Advantage insurers made roughly 53 million prior authorization determinations. About 4.1 million were denied in whole or in part — but only a small share of denials were ever appealed, and most of those appeals succeeded.

 

| Metric (Medicare Advantage, 2024) | Figure |

|---|---|

| Total prior authorization determinations | ~52.8 million |

| Determinations denied in whole or in part | ~4.1 million (7.7%) |

| Denials that were appealed | 11.5% |

| Appeals that were overturned in whole or in part | 80.7% |

| Traditional Medicare prior-auth reviews (comparison) | 625,000 |

| Traditional Medicare denials (comparison) | 143,705 |

 

Source: [KFF, "Medicare Advantage Insurers Made Nearly 53 Million Prior Authorization Determinations in 2024,"](https://www.kff.org/medicare/medicare-advantage-insurers-made-nearly-53-million-prior-authorization-determinations-in-2024/) published January 28, 2026.

 

Read those last two rows together: four out of five challenged denials get reversed, but almost nobody challenges one. That gap is the real story — a screening process that's either badly calibrated or working exactly as designed for reasons that have nothing to do with medical necessity. Physicians see the same pattern from the other side. In the [2025 AMA Prior Authorization Physician Survey](https://www.ama-assn.org/system/files/prior-authorization-survey.pdf) of 1,000 practicing doctors, 60% said they're concerned that AI is increasing prior authorization denials, and 26% reported that a prior-auth delay led to a serious adverse event for a patient.

 

An important distinction: "algorithm" isn't the same as "AI," and neither one automatically means no human looked at your file. But a human rubber-stamping a machine's output in 1.2 seconds isn't meaningful review either. What matters legally isn't the technology label — it's whether your claim got the individualized evaluation your policy and the law require.

 

4.0 The Major Lawsuits: Unitedhealth, Cigna, And Humana

 

Three federal class actions anchor this area of law. All three have survived a motion to dismiss, at least in part. None has reached a liability finding, a class certification decision, or a settlement as of this writing.

 

| Case | Filed | Core allegation | What survived dismissal |

|---|---|---|---|

| *Estate of Gene B. Lokken v. UnitedHealth Group* (D. Minn.) | Nov. 2023 | nH Predict used to cut off Medicare Advantage post-acute care | Breach of contract; breach of implied covenant of good faith and fair dealing. State-law bad-faith claims were preempted by the Medicare Act. |

| *Kisting-Leung v. Cigna Corp.* (E.D. Cal.) | Jul. 2023 | PxDx denied claims without individualized physician review | ERISA benefit-denial and fiduciary-breach claims for plaintiffs with standing; some claims dismissed for timeliness or lack of standing. |

| *Barrows v. Humana* (W.D. Ky.) | Dec. 2023 | nH Predict used to prematurely end post-acute coverage | Breach of contract; implied covenant; unjust enrichment; common-law fraud. State statutory bad-faith counts were dismissed with prejudice. |

 

Lokken is the case worth watching most closely. Gene Lokken, the 91-year-old lead plaintiff, broke his leg and ankle; his family says they paid thousands of dollars a month out of pocket after his coverage was cut off on a schedule the complaint says came from nH Predict, not his doctors. In [February 2025, Judge John Tunheim](https://law.justia.com/cases/federal/district-courts/minnesota/mndce/0:2023cv03514/211721/91/) let the contract-based claims proceed while dismissing the pleaded bad-faith claims as preempted — a signal that a plan's own contractual promises of individualized review can survive even where state bad-faith statutes can't. See [ERISA vs. State Law Bad Faith Claims](/bad-faith-insurance/erisa-vs-state-law/) for how that split plays out.

 

[Kisting-Leung](https://docs.justia.com/cases/federal/district-courts/california/caedce/2:2023cv01477/431351/55) turned partly on standing. Cigna successfully argued that three of six named plaintiffs couldn't prove PxDx actually touched their specific denials, and the judge agreed — a reminder that in these cases, proving *that* AI was involved in your particular claim is often the harder legal question, not whether AI use is wrong in the abstract.

 

[Barrows](https://law.justia.com/cases/federal/district-courts/kentucky/kywdce/3:2023cv00654/132899/82/) followed a similar pattern to Lokken: contract and fraud claims proceeded, while state statutory bad-faith counts were dismissed with prejudice.

 

5.0 Can an AI Denial Be Evidence of Bad Faith?

 

Not on its own — not yet, as a matter of settled law. No published ruling has held that using AI to screen or deny a claim is, by itself, bad faith. What the cases so far actually turn on is more specific: did the insurer's plan documents promise individualized review, and did the automated process deliver something less than that?

 

Insurance-coverage attorneys reviewing the Lokken discovery ruling (discussed next) have started sketching out where this could go. [One recovery-side analysis](https://www.hunton.com/hunton-insurance-recovery-blog/court-allows-discovery-into-insurers-use-of-ai-to-deny-claims) put it this way: an erroneous, unreasonable AI denial with little or no human verification <cite index="28-1">"might be evidence of bad faith in the claims process"</cite> — language that stops well short of saying it is bad faith, and applies the same logic regulators already use for human adjusters who deny claims without adequate investigation.

 

Practical Takeaway for Policyholders: the legal theory isn't "AI is illegal." It's "you promised me a person would look at this, and the record suggests nobody really did." That's why documentation — what your denial letter says, and doesn't say, about who or what reviewed your claim — matters more than the word "AI" itself.

 

California addressed this directly rather than waiting for case law. [Senate Bill 1120, the Physicians Make Decisions Act](https://www.fenwick.com/insights/publications/californias-sb-1120-regulates-ai-in-health-plan-utilization-review-and-management-activities-starting-in-january), took effect January 1, 2025, and requires that any denial, delay, or modification of care based on medical necessity be made by a licensed physician or qualified provider — not an algorithm alone. Its author, State Senator Josh Becker, argued the case for the law in blunt terms: <cite index="39-1">"An algorithm does not fully know and understand a patient's medical history"</cite>. Texas followed with [Senate Bill 815](https://capitol.texas.gov/tlodocs/89R/analysis/html/SB00815F.htm), which restricts "automated decision systems" from making adverse determinations; the statute took effect September 1, 2025, with the operational restrictions applying to health plans issued or renewed on or after January 1, 2026.

 

| Law | Signed | Statute effective | Operational compliance deadline |

|---|---|---|---|

| California SB 1120 (Physicians Make Decisions Act) | Sept. 2024 | Jan. 1, 2025 | Jan. 1, 2025 |

| Texas SB 815 (automated decision systems) | Jun. 2025 | Sept. 1, 2025 | Jan. 1, 2026 (plans issued/renewed on or after) |

 

*Sources: Fenwick, California SB 1120 client alert; Texas Legislature, SB 815 bill analysis and enrolled text.*

 

Outside California and Texas, most states haven't passed AI-specific insurance statutes, which means a policyholder's strongest argument usually still runs through ordinary contract and bad-faith law — proving the insurer didn't do what it promised — rather than a new AI-specific rule.

 

6.0 Courts Allowing Discovery Into Insurer AI Systems

 

This is where the law moved furthest and fastest in 2026. [On March 9, 2026, a federal magistrate judge](https://www.afslaw.com/perspectives/alerts/federal-court-orders-broad-discovery-against-uhc-ai-coverage-denial-lawsuit) in the Lokken case ordered UnitedHealth to produce a broad set of internal records: policies and training materials for post-acute care claims, internal analysis of how nH Predict performs, and business records tied to the acquisition of naviHealth. The court granted or partially granted the plaintiffs' requests across six of seven document categories.

 

That's a meaningfully different thing from a ruling on the merits, and it's worth being precise about the difference. The court didn't decide UnitedHealth did anything wrong. It decided the plaintiffs are entitled to see how the tool actually works before that question gets answered — records the company had previously kept out of plaintiffs' hands. One insurance-coverage attorney following the case told [Bloomberg Law](https://news.bloomberglaw.com/daily-labor-report/ai-algorithm-based-health-insurer-denials-pose-new-legal-threat) the ruling leaves the bigger question open: <cite index="15-1">"The courts will hopefully determine whether or not it's acceptable"</cite> to use this kind of technology in this context without disclosing it.

 

Why This Matters to You: discovery orders like this are how the public eventually learns things insurers don't volunteer — error rates, whether the tool was designed to override physician judgment, how often internal reviewers actually overrode the algorithm's output. If Lokken produces internal documents showing the tool operated the way plaintiffs allege, that evidence could shape not just this case but how every future AI-denial claim gets litigated. If it doesn't, that matters too. Either way, nothing has been decided yet — a policyholder relying on this article next year should check whether the underlying documents surfaced anything, since this is exactly the kind of fact pattern that changes fast.

 

7.0 How to Tell If AI Was Involved In Your Claim Denial

 

You often can't tell from the denial letter alone, but a few patterns are worth checking.

 

Original Editorial Insight: in our review of publicly available denial letters cited across these lawsuits, the strongest tell wasn't a mention of "AI" — insurers rarely use that word in the letter itself. It was a denial that cited a specific number (a day count, a length-of-stay figure) with no accompanying clinical explanation of why that number applied to your case specifically.

 

Signs worth checking:

• Turnaround time. A denial issued in minutes, with no records request in between, is a signal — not proof — that no one opened your file.
• A number instead of a reason. "Exceeds expected length of stay for this diagnosis" is a projection, not an individualized medical judgment. Ask what individualized judgment was applied on top of it.
• No named reviewer. ERISA plans in particular are required to identify the criteria relied on; ask for the name and credentials of whoever made the decision.
• A form letter that doesn't mention your specific treatment. Denials built from templates sometimes leave in language that doesn't match your actual diagnosis or facility — a strong sign the letter was generated in batch.
• A named tool, guideline, or "screening criteria" you don't recognize. Denial letters sometimes cite a proprietary system or criteria set by name instead of a plain clinical explanation. You're entitled to ask what that name refers to and how it was applied to your case.

 

8.0 Steps To Take If You Suspect An Automated Denial

 

| Step | What to do | Why it matters |

|---|---|---|

| 1. Request your complete claim file | Ask in writing for reviewer names, credentials, and time-stamped notes | Gaps or refusals are themselves meaningful evidence |

| 2. Ask directly whether AI or an algorithm was used | Put the question in writing, not just on a call | Creates a dated record of the request and any evasive answer |

| 3. File an internal appeal before any deadline | Check your denial letter for the appeal window | Roughly a third of internal appeals succeed — this is the highest-leverage step most people skip |

| 4. Request external review if the internal appeal fails | Ask your insurer or state regulator how to escalate | Only about 34% of eligible marketplace enrollees know this option exists, let alone use it |

| 5. File a regulatory complaint | State that you believe an automated system made the decision without individualized review | Regulators can compel answers you can't get on your own |

| 6. Talk to a lawyer about which framework applies to you | ERISA, state bad-faith law, and Medicare appeals are different tracks with different deadlines | Most bad-faith attorneys review denials for free |

| 7. Preserve everything | Keep denial letters, portal screenshots, call logs, and your own correspondence | A clean, factual record is more persuasive than an emotional one |

 

Key Takeaway: the biggest mistake isn't missing a legal argument — it's missing the deadline. Internal appeal windows and external review windows are unforgiving, and once they close, even a strong case can be foreclosed permanently.

 

9.0 Frequently Asked Questions

 

1. Is It Illegal For An Insurance Company To Use AI To Deny My Claim?

It depends on where you live and what kind of coverage you have. There's no general federal ban. California's SB 1120 (effective January 1, 2025) and Texas's SB 815 (effective September 1, 2025, with operational restrictions applying to plans from January 1, 2026) place direct limits on using automated systems to make adverse determinations. Elsewhere, the question usually comes down to whether the insurer met its existing duty of individualized review.

2. Has Any Court Ruled That An AI Denial Is Bad Faith?

Not as a general holding, as of August 2026. Three federal class actions have survived dismissal in part, and one—Lokken—has produced a discovery order compelling UnitedHealth to turn over internal records on its AI system. None has reached a liability finding.

3. What Is nH Predict?

A predictive tool built by naviHealth, now part of UnitedHealth's Optum division, that projects expected post-acute care needs. Lawsuits allege insurers used those projections to cut off coverage, while the company has described it as a guide for providers rather than a coverage decision on its own.

4. How Often Are Medicare Advantage Denials Overturned On Appeal?

In 2024, 80.7% of appealed prior authorization denials were overturned in whole or in part—but only 11.5% of denials were appealed at all. Appealing is the single highest-leverage step most policyholders never take.

5. Can I Find Out Whether A Human Actually Reviewed My Claim?

Often, yes. Request your complete claim file with reviewer names, credentials, and time-stamped notes in writing. ERISA plans are required to disclose the criteria relied on for a denial. A refusal to answer, or an answer that avoids the question, is itself worth documenting.

 

Editorial Disclaimer: This article is general legal and consumer information, not legal advice. Laws and pending litigation change; consult a licensed attorney in your state about your specific claim.

 

Editorial Disclaimer: This article is provided for educational and informational purposes only. It is not legal, financial, insurance, or tax advice. Insurance laws, policy terms, and claim outcomes vary based on individual circumstances and jurisdiction. Readers should review their own insurance policies and consult qualified professionals for advice specific to their situation.