ArchivedThe 4 D's rubric is kept here for reference. Current program work uses the three-risk readiness approach.
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Crinkle HealthProduction readiness

The 4 D's rubric for shipping the Medicare AI

A shared checklist for product, engineering, compliance, and operations to decide what is validated, what is still risky, and what work remains before any real member interacts with the recommendation and enrollment experience.

0 of 50 items touched
Validated · 0 Partial · 0 Not started · 50 Blocked · 0

Click any item's status chip to cycle Not started → Partial → Validated → Blocked. State is local to this demo session.

Discovery

What we must know before building

Foundational questions

Align on the member problem, the current journey, and the definition of ready before writing a line of production code.

1.1

What member problem are we solving, and for whom?

Why: Avoid building a faster path to the wrong product.

Evidence: User research synthesis; personas (e.g., Margaret, the caregiver proxy); problem statement canvas.

Risk if skipped: Scope creep; features that impress but don't move enrollment quality.

1.2

What is the current member journey today?

Why: Baseline to measure improvement and identify handoff points.

Evidence: Journey map + baseline metrics (NPS, call handle time, conversion rate, error rate).

Risk if skipped: No credible ROI; cannot detect regression.

1.3

Which regulatory bodies govern this experience?

Why: Determines non-negotiable compliance gates.

Evidence: Regulatory matrix; TPMO, SOA, marketing, privacy, licensing requirements.

Risk if skipped: Fines, carrier contract termination, marketing suspension.

1.4

What existing data, systems, and contracts do we have?

Why: Prevents duplicate infrastructure and reveals integration constraints.

Evidence: Systems inventory; data dictionaries; API contracts; licensing agreements.

Risk if skipped: Underestimated integration cost; data rights issues.

1.5

What is the definition of "ready for production"?

Why: Aligns the VP and team on the decision gate.

Evidence: Signed readiness criteria and exit thresholds (accuracy ≥ 95%, completion ≥ 60%, hallucination < 1%).

Risk if skipped: Subjective launch decisions; hidden disagreements.

1.6

Who owns the final recommendation?

Why: Shapes liability, disclaimers, and escalation paths.

Evidence: RACI matrix; legal review of liability language.

Risk if skipped: Ambiguous accountability when a recommendation is wrong.

Outcomes the VP should sign off on
  • Target member segments and their primary jobs-to-be-done.
  • Baseline conversion and satisfaction metrics for the current journey.
  • List of regulatory gates that are hard constraints vs. optimization goals.
  • Approved success metrics and minimum acceptable thresholds.
  • Clear ownership of the AI recommendation, the enrollment app, and the licensed agent handoff.
Full markdown source: documents/plans/production-readiness-rubric.md