AZIS R. DABAS

Healthcare strategy
AI + operating leadership

Operating recordMandates
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Work02 / Executive mandate

Healthcare AI, payer-facing, Medicaid, Medicare Advantage, payment integrity, and government-adjacent healthcare teams

Government Payment Integrity and Policy-to-Workflow Design

Unequal ivory and terracotta mineral forms held together by a continuous tensile membrane
StewardshipCapital & shared risk

Government healthcare growth design that connects policy signal, payment integrity, procurement narrative, AI workflow governance, human review, and audit-ready proof.

Read the operating brief
Proposed mandate workflow

From policy signal to an audit-ready buyer case

  1. Interpret policy pressure

    Turn CMS, Medicaid, payer, and procurement signals into a focused thesis.

  2. Map payment-integrity need

    Locate leakage, improper-payment risk, utilization friction, and provider burden.

  3. Design reviewed workflows

    Bound AI recommendations with evidence packets, escalation, and audit trails.

  4. Shape procurement narrative

    Connect the value proposition to measurable, compliant implementation requirements.

  5. Close the proof loop

    Evaluate savings, appeals, provider abrasion, audit findings, and cycle time.

Proposed service workflow: policy, procurement, operations, compliance, and proof are designed as one system.

The buyer problem

The issue underneath the visible activity.

This is the problem leadership must make legible before adding more pipeline, tooling, headcount, or implementation burden.

Government healthcare growth fails when policy, procurement, claims operations, compliance, product, and implementation run as separate workstreams. The buyer needs a governed operating model, not a louder pitch.

What gets built

A working management system, not a recommendation left in a deck.

The scope is organized around the artifacts, operating rules, and decision cadence the team needs to keep using after the engagement.

  1. 01

    Policy signal intake that turns CMS, Medicaid, payer, and procurement pressure into a focused market thesis.

  2. 02

    Claims/payment integrity opportunity map across leakage, improper payment risk, utilization friction, provider abrasion, and operational lift.

  3. 03

    AI-enabled workflow architecture with bounded recommendations, evidence packets, human review, escalation controls, and audit trails.

  4. 04

    RFP/RFI and contract narrative that makes the value proposition specific, measurable, compliant, and implementation-ready.

  5. 05

    Proof loop across savings, appeal rates, provider abrasion, audit findings, cycle time, and operating lift.

Proof patterns

What leadership should be able to observe.

  1. 01

    Claims forensics translated into account and service-line opportunity.

  2. 02

    Payer/VBC and provider-network fluency across reimbursement, care gaps, and value proof.

  3. 03

    Governed AI/RevOps architecture where AI assists workflow while humans own patient-impact decisions.

Decision questions

What the executive room must answer.

  1. 01

    Which policy or payment-integrity pressure creates a contractable buyer problem?

  2. 02

    What claims, workflow, and audit evidence would make the value defensible?

  3. 03

    Where can AI safely recommend, draft, route, or monitor without replacing human judgment?

  4. 04

    How should procurement, legal, finance, product, and operations hear the same story?

Trust boundary

What this mandate will not pretend away.

  • Do not treat government healthcare as generic enterprise sales.
  • Do not position healthcare AI as black-box autonomy.
  • Do not separate legal/compliance posture from the commercial and implementation narrative.

Related proof

These records are contextual proof paths, not blanket client-outcome claims. Evidence class and claim boundary are shown from the public case record where available.

01

Operating architecture

Government Payment Integrity Control Plane

Policy, procurement, claims intelligence, AI governance, and proof in one pursuit system
Claim boundary

This is an executive strategy and operating-blueprint build. It does not claim federal procurement authority, employment, contract awards, or implemented government delivery outcomes.

02

Operating architecture

Reimbursement Infrastructure

Prior authorization, claims workflow, payer friction, and AI control in one operating layer
Claim boundary

This is a de-identified reimbursement and commercialization architecture based on public policy and workflow analysis. It does not claim company affiliation, product deployment, customer results, or realized savings.

03

Employer outcome

Databricks Claims Forensics / Referral Leakage

Claims signal -> account strategy
Claim boundary

Aggregate results are reported in the September 12, 2026 executive resume. The $125.9M leakage figure is a modeled opportunity and $22M downstream LTV is an estimate, not realized or booked revenue. Account-level and patient-level records remain private.

Connected context

Related mandateAI Solutions Design and RCM OrchestrationRelated mandateHealthcare AI Consulting and Implementation

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