Payers Made Healthcare AI a Liability. Providers Must Make It Accountable.
Insurers trained models on patients' own records, then used them to deny care. The lawsuits and the new state laws followed.
By Paul J. Swider | Healthcare AI & HealthTech Leader | June 2026
For more than a decade, the largest health insurers and their technology partners quietly built some of the most powerful machine learning systems in America—not to save lives, but to predict, automate, and sometimes override care for millions of patients. They did it with your data, the claims, records, and bills that hospitals and doctors sent them every single day.
The results? Algorithms that allegedly denied rehab for elderly patients in nursing homes, with over 90% of those denials later overturned on appeal. One system reportedly batch-denied claims in 1.2 seconds without opening patient files. Senate investigators documented sharp spikes in post-acute care denials exactly when the automation scaled. Lawsuits are advancing. Doctors say AI is making prior auth hell worse.
And the kicker, the same companies (and their data empires) that powered this are now standing on stages and in policy rooms preaching about “responsible AI safety” and “ethical clinical AI” for the tools that will guide your doctor’s decisions and your family’s care.
The irony is unmistakable. The data flywheel that powered one side of the industry is now being positioned to guide the other. For healthcare leaders who sit at the intersection of providers, payers, technology, and patients, this moment demands a clear-eyed look: not to assign blame, but to ensure the next wave of AI earns the trust it requires to improve outcomes at scale.
The Data Foundation: Where It All Began
Virtually 100% of the training data for these payer-side models originated in the healthcare delivery system. Hospitals and providers generated encounters, documentation, coding, and claims (837 files), which flowed through clearinghouses like Change Healthcare (acquired by Optum in 2022) and directly to payers. Optum’s own materials describe repositories spanning 20+ years and roughly 240–285 million de-identified lives, including billions of lab results, procedures, and diagnoses.
Change Healthcare processed ~15 billion transactions annually before the 2024 cyberattack, touching roughly one-third of U.S. patient records. Providers contributed both indirectly (through every claim submitted) and directly (through RCM contracts, EHR integrations, and analytics partnerships). Optum sources EHR data directly from provider organizations across the country.
This created a flywheel: more provider data → richer models → more powerful predictions on approval likelihood, denial risk, and cost trajectories.
The Controversy: Documented Challenges and Human Impact
Public evidence is substantial. The Senate Permanent Subcommittee on Investigations report (October 2024) detailed how UnitedHealthcare, Humana, and CVS saw post-acute denial rates surge as they deployed algorithmic tools, with UnitedHealth’s skilled nursing denial rate increasing dramatically in some categories. Read the full Senate PSI report here.
Class-action litigation against UnitedHealth/naviHealth’s nH Predict tool alleges a ~90% overturn rate on appealed denials for post-acute care. The case has advanced, with courts ordering discovery. Similar scrutiny hit Cigna’s PxDx system (ProPublica investigation documented 300,000+ claims denied in batches averaging 1.2 seconds). STAT investigation and ProPublica report.
Government data reinforces the pattern: When Medicare Advantage prior-authorization denials are appealed, 80–95% are overturned (KFF and HHS OIG analyses). The AMA’s 2025 physician survey found 61% of doctors fear payer AI is increasing denials and contributing to burnout. AMA 2025 Survey (PDF).
The Regulatory Catch-Up: 2024–2026 Brought Teeth
Meaningful guardrails arrived with force starting in 2024. CMS’s Interoperability and Prior Authorization Final Rule (CMS-0057-F) requires faster decisions, specific denial reasons, and transparency metrics beginning 2026. CMS also issued FAQs stating algorithms may assist but must comply with individualized medical necessity rules. CMS Final Rule details.
States moved aggressively: California (2024), followed by Indiana, Utah, Washington, Arizona, Maryland, Georgia, and others in 2025–2026, prohibiting AI as the sole basis for medical-necessity denials and requiring clinician review or disclosure.
The Irony: From Controversy to “AI Safety” Leadership
Today, the same organizations publish white papers, join pledges, and speak at conferences about responsible AI governance, fairness boards, and ethical clinical AI. Optum emphasizes explainable models and human oversight in public statements. Meanwhile, they continue to expand clinical analytics and decision-support offerings to providers.
This is not hypocrisy in a vacuum, it is the natural outcome of vertical integration and data advantage. The same records that trained denial models now power (or could power) tools that help clinicians. The question for leadership is whether we will apply the hard lessons from the last decade transparently.
Implications and a Path Forward for Healthcare Leaders
As a healthcare AI and HealthTech leader serving hospitals, systems, and innovators, I see three clear imperatives:
Acknowledge the full data story. Providers generated the data. Payers scaled it. Everyone benefits when we own the history instead of sanitizing it.
Demand (and build) verifiable governance. Insist on independent validation, clinician-in-the-loop requirements, audit logs, and outcome transparency for clinical AI, exactly what was missing earlier.
Collaborate across the aisle. Payers, hospitals, EHR vendors, and startups must co-create standards that protect patients while unlocking AI’s potential. The market will reward those who lead with candor and evidence.
The data paradox is real, but it does not have to define the future. Healthcare leaders who confront it honestly, while driving practical, patient-centered solutions, will be the ones trusted to guide clinical AI’s next decade.
What are your thoughts? Have you seen this tension play out in your organization? Drop a comment or reach out, I read every one.
Sources & Further Reading (all hyperlinked)
Senate PSI Report on Medicare Advantage Denials (Oct 2024): Full PDF
STAT Investigation on UnitedHealth Algorithm (2023): Read here
ProPublica on Cigna (2023): Investigation
KFF MA Prior Auth Data (2026 update): KFF Analysis
AMA 2025 Prior Authorization Survey: PDF
CMS 2024 Prior Auth Final Rule: Details
Optum Data Assets Reference: OptumIQ document
Hashtags for sharing: #HealthcareAI #HealthTech #PriorAuth #AISafety #MedicareAdvantage #DataGovernance #HealthcareLeadership #ResponsibleAI
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