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PAN Lab example

Tennessee TennCare TEDS

The notice that never came: an automated Medicaid eligibility system

An automated system decides Medicaid eligibility and runs renewals on its own, then generates the termination notices that are supposed to let people appeal. Modeled on Tennessee's TEDS. When the notice is misleading, deficient, or sent to the wrong household, the right to a hearing exists on paper and never reaches the person; correction flows only to those with the luck and lawyering to force it.

Stylized model of a documented deploymentPublic benefits & eligibility

Open this example in PAN Lab v0.1 to apply pressures and levers and watch what the system does.

What this models

This example runs on the TEDS-class automated eligibility-and-notice system network: 6 components and 10 pathways between them. Every context in the Lab is a stylized model, never a reconstruction of any actual deployment, and each assumption behind it carries a provenance label.

Evidence base: 4 assumed · 2 published baseline. In the Lab, the shaded evidence band behind each headline readout draws its width from the least-established class below.

  • assumed

    This models the automated-eligibility-and-notice pattern documented in the Tennessee TEDS (A.M.C. v. Smith) case file — not a reconstruction of the actual system.

  • baseline

    The correction loop that mattered here is the Medicaid fair hearing: a statutory right that existed in law and was severed in practice by misleading, deficient, and misrouted notices and a withdrawn good-cause override — so the notice-to-hearing trigger runs weak at baseline.

  • baseline

    The store-to-model re-feed encodes single-error propagation: converted legacy records, including a documented keying error, re-enter every automated ex parte renewal, so one bad record compounds rather than averaging out.

  • assumed

    The record-side reconciliation check is drawn on the map but runs dry at baseline: terminations were actioned and notices dispatched with nothing verifying that a valid, understandable notice reached the enrollee before coverage ended — the due-process check the court found missing, and the gap the replication-reconciliation lever closes.

  • assumed

    The court's ADA finding that people with disabilities faced additional, unequal burdens, and the outcomes of real terminated enrollees, are documented in the case file and measured outside any diagram like this one. This Lab models institutional propagation, not demographics or client outcomes, and estimates no benefit change for any served person.

  • assumed

    The vendor-side defect-remediation loop is externalized from this map: fixes to the engine itself run through vendor change orders documented as taking months to years and hundreds of thousands of dollars per defect, so the only correction pathways drawn here are the case-level ones (slow caseworker fixes and the thin hearing loop). The vendor-gate lever is where that external fix loop is governed.

What this example does not show

  • The documented harm is loss of Medicaid coverage for real enrollees, with people with disabilities found to face additional, unequal burdens. The Lab models institutional propagation, not client outcomes or demographics, and estimates no benefit change for any served person; that harm is documented in the case file and measured outside any diagram like this one.

Sources and evidence

What this example rests on, claim by claim. Every entry resolves to the same ledger the Evidence Registry publishes.

  • In A.M.C. v. Smith (No. 3:20-cv-00240, M.D. Tenn.), a federal court held after a five-day bench trial that Tennessee's Deloitte-built TEDS automated Medicaid eligibility system, operational statewide since March 19, 2019 for a program covering roughly 1.7 million residents, produced wrongful terminations, wrong-household assignments, and misleading or missing notices that violated the Medicaid Act, the Fourteenth Amendment's Due Process Clause, and the Americans with Disabilities Act; the 116-page opinion, issued August 26, 2024 by Judge Waverly D. Crenshaw Jr., ordered mediation before considering an injunction.

    empirical
    • Trade press StateScoop (Keely Quinlan), Automated Medicaid system contributed to thousands losing health care coverage (2024) https://statescoop.com/tenncare-automated-medicaid-healthcare-coverage-2024/
    • Trade press Stotler Hayes Group LLC (Erin Sailor), Holding State Medicaid Agencies Accountable: A Federal Court Issues Ruling on Deficiencies and Discrimination in TennCare (2024) https://stotlerhayes.com/holding-state-medicaid-agencies-accountable-a-federal-court-issues-ruling-on-deficiencies-and-discrimination-in-tenncare/
    • Academic Georgetown University Center for Children and Families (Leonardo Cuello), Federal Judge in Tennessee Sides with Individuals Terminated from Medicaid (2024) https://ccf.georgetown.edu/2024/09/06/federal-judge-in-tennessee-sides-with-individuals-terminated-from-medicaid-finds-numerous-violations-in-tennessee-medicaid-eligibility-process/
    • Advocacy National Health Law Program, Major Litigation Win: Court Rules Tennessee's Medicaid Program Wrongfully Denied Health Care for Thousands (2024) https://healthlaw.org/news/major-litigation-win-court-rules-tennessees-medicaid-program-wrongfully-denied-health-care-for-thousands/

Where this connects

Institutional pressures in this domain

  • Austerity & recovery incentives — Cost-cutting and overpayment-recovery targets tilt the system toward denial and enforcement errors.
  • Vendor opacity — The deploying institution cannot inspect the model, data, or update pipeline it is accountable for.
  • Compliance over substance — Paper controls (sign-offs, checklists) satisfy audits while the behavior they describe erodes.
  • Reviewer bottleneck — One fixed-capacity checking stage sits between AI output and consequence; everything queues behind it.

All of them in context on the Public benefits & eligibility domain page.

Levers available here and the patterns behind them

Documented case histories