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Vardr Partners
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02State Medicaid

Medicaid eligibility systems that stop ejecting people who qualify.

More than 14% of state Medicaid disenrollments in recent renewal cycles have been administrative — paperwork failures, not eligibility losses. The eligibility engine itself is rarely the cause. The seam between the eligibility engine, the renewal letter, and the verification workflow is the cause. We work on that seam.

Annual Medicaid spend
$870B+
Eligibility re-determinations
94M / yr
Admin-disenrollment share
≈ 14–28%

01What we keep seeing

Three failure modes that pre-date the AI conversation.

  • 01

    Cliff effects in eligibility re-determination

    A correct policy change to MAGI or income lookback shouldn't be the same project as the eligibility-engine rewrite. They usually are, because policy + engine are entangled in code with no versioning between them.

  • 02

    Provider-overbilling detection runs once a quarter

    Most state OIGs detect overbilling retrospectively through claims audits. By the time a finding hits the recovery pipeline, the overbilled funds have been out the door for ten months.

  • 03

    Adverse-action notices fail FOIA-grade review

    Generated notices reference the system in generic terms. The first due-process challenge surfaces the inadequacy; denials are vacated en masse.

02How we work the seam

Specific practices. Specific outcomes. No platitudes.

  1. Practice 01

    Outcome

    Cut administrative disenrollments by 30–60%

    Renewal-letter strangler

    Replace the renewal-letter generator with a versioned templating layer that references the policy text bound to the renewal date. Adverse-action notices become defensible and the same template feeds the claimant-portal copy.

  2. Practice 02

    Outcome

    Catch overbilling before, not after, payment leaves

    Pre-payment provider-overbilling signal

    Move the overbilling check from the quarterly audit cycle into the pre-payment workflow. Use graph signals (provider-to-NPI-to-bank-account) and sequence models on prior-period claims to score each submission at intake.

  3. Practice 03

    Outcome

    Every denial reconstructible per regulation

    Eligibility-decision provenance

    Capture facts-of-claim, policy version, feature snapshot, model artifact, and caseworker action for every Medicaid decision. Required for OIG defense and for the AI-influenced adverse-action audit trail OMB will eventually require.

03Market scale

The size, growth, and obtainable share of this market.

Every benefits-decisioning segment shares the same pattern: program dollars in the hundreds of billions, services spend in the single-digit billions, modernization and detection investments compound across the program spend.

TAM

$870.0B

Program dollars in scope

SAM

$6.5B

Annual services spend

5-yr CAGR

6.2%

Rolling SAM growth

3-yr SOM

$180M

Vardr-obtainable

Vardr captures a small but compounding share of the addressable services spend; the underlying program dollars dwarf the modernization budget.

TAM
$870.0B
SAM
$6.5B
3-yr SOM
$180M

CMS National Health Expenditure (Medicaid line). SAM = state IT and consulting modernization spend on eligibility, integrity, and analytics. SOM = 3-yr Vardr-obtainable across priority states.

5-year SAM growth — State Medicaid

$6.5B$8.8B

$0$2.2B$4.4B$6.6B$8.8B202620272028202920302031

04Programs we focus on

State Medicaid eligibility and program integrity.

  • State Medicaid Agency

    Medicaid eligibility (MAGI + non-MAGI)

    Eligibility engines, renewal automation, MAGI calculations, work-requirement integrations where applicable.

  • State Medicaid / CHIP agency

    CHIP enrollment

    Cross-program eligibility (Medicaid ↔ CHIP), seamless transitions at income changes.

  • State Medicaid Agency

    Provider enrollment and screening

    Provider risk-screening, NPI verification, ownership-disclosure tracking, exclusion-list monitoring.

  • State OIG / Inspector General

    Program-integrity / OIG analytics

    Investigator queues, expected-recoverable-value ranking, adverse-action documentation pipeline.

05Questions worth asking

Open these with us — or with anyone else.

We bring these to every program-office conversation. Use them whether or not we end up working together.

  1. 01

    Can the engine retrieve the policy text bound to a specific renewal decision from three years ago?

  2. 02

    What percentage of disenrollments in the last cycle were administrative (procedural), and what is your current trajectory to bring it under 10%?

  3. 03

    Can a provider's overbilling signal arrive before payment, or only after?

  4. 04

    What does your AI-influenced adverse-action notice say — and has counsel reviewed it?

Working in state medicaid and want our take?

45-minute principal-level briefing. Bring the program, the constraint, the deadline.