Claims-Denial Practice Case — Annotated Strong Answer

This is one defensible reasoning path through the fictional practice case, not anonymous ground truth. Alternative answers are acceptable when they preserve the evidence classes, authority, limitations, and decision thresholds in the source pack.

Complete your own first pass before using this calibration aid.

Use the staged case hub and reveal only the source for the mission you are completing. This answer intentionally exposes the complete chronology.

1. Field view

Strong claim:

In two observed NorthLake cases, an authenticated specialist inspected source evidence, changed or completed the proposed correction, approved submission, and relied on claims-system readback. The two cases do not establish frequency.

Why it is strong: it states occurrence, scope, and limitation. It does not turn Maya's “every modifier correction” statement into measured frequency.

Next field move: assign the policy owner to confirm revision 8 applies to the pilot service boundary and observe or query additional NorthLake modifier cases. Do not begin Summit design from a NorthLake exact-build test.

2. Material reframe

Inherited claim: the pilot automatically resubmits eligible corrected claims for NorthLake and Summit.

Contesting evidence:

• S4 requires claim- and revision-scoped specialist approval and readback.
• S3 shows specialist judgment changed both reviewed corrections.
• S5 shows a material NorthLake modifier defect and duplicate behavior.

Proposed reframe:

Phase one is limited to the evidence-supported NorthLake cohort. The service may normalize, retrieve cited evidence, and recommend a correction. An authenticated specialist retains approval and submission in the workbench. Summit and automatic submission remain excluded until separately evidenced and authorized.

Safe fallback: continue existing specialist correction and submission; use the service in observe/recommend mode only after duplicate recommendation is fixed.

Disposition: continue discovery until policy applicability, metric verification, label authority, and the duplicate failure are resolved. A later bounded kickoff may authorize the recommendation-only slice. Product Security cannot accept the business outcome; the Revenue Cycle VP cannot waive the technical effect boundary alone.

3. Outcome and economics

• Decision event: next-phase funding recommendation on 20 Aug.
• Eligible workflow: NorthLake inpatient medical-necessity denials meeting the documented July query and having permitted source access.
• Exclusions: Summit, automatic submission, missing required evidence without manual routing, unsupported payer behavior.
• Baseline: 42-minute median receipt-to-reviewed-correction for the July cohort.
• Sold acceptance target: 30 percent average handling-time improvement under S1, without an attached baseline or metric contract. Proposed interim four-week continuation floor: at least 15 percent reduction in median receipt-to-reviewed-correction time and at least 70 percent adoption. The measures are not interchangeable, and the interim floor does not amend the sold target. Only the proposed interim contract adds zero unauthorized submissions, no error increase under independently adjudicated labels, and complete operating cost below CAD 18 per eligible case; S1 contains no such guardrails.
• Counterfactual: existing specialist-led correction plus deterministic repair of the NorthLake adapter. The AI proposal must beat that smaller intervention.

Blocking gates:

• Data: correction-label authority and adjudication are unknown; 18 records lack linked notes.
• Process: specialist workflow is observed, but representative frequency and Summit workflow are unknown.
• Governance: policy applies on its face, but the policy owner must confirm the pilot service boundary.
• Accountable capability: release and operating owners exist; independent metric verification is unassigned.

Why a generic “data readiness score” would be weak: the pack contains data, but the decision-bound labels, missing-note behavior, and source authority are unresolved.

4. Smallest mechanism hypothesis

Sources S1–S6 do not prove the mechanism. A defensible bake-off starts with these hypotheses:

• Test deterministic rules for stable denial-code normalization and policy invariants; S5 supports only the tested modifier fixture.
• Test scoped retrieval for permitted, current encounter and payer-policy passages, with a separate missing-note route.
• Compare a bounded model recommendation with deterministic and human-only routes on quality, latency, partial cost, and review burden.
• Keep correction approval and submission with the authenticated specialist in every candidate.
• Add no bounded agent unless measured multi-step uncertainty defeats the smaller routes.

The provisional leading route is rules plus scoped retrieval, a bounded model call, and specialist review. It becomes selected only if the bake-off shows it satisfies the stated requirements better than simpler candidates.

Invalidation triggers: deterministic coverage becomes sufficient, citation or correction quality falls below threshold, review capacity saturates, cost exceeds the ceiling, policy changes, or adoption remains below the decision threshold.

5. Data, state, and dependency design

Canonical records: case revision, evidence-bundle revision, recommendation revision, specialist disposition, submission approval, and claims-system readback. Model output is a proposal, not canonical approval.

Critical typed relationships:

Data/context contract: purpose-bound NorthLake sources, named source and label authority, preparation lineage, revision/freshness checks, restricted-field handling, retention, and manual routing when required context is missing or contested.

6. Evaluation argument

rec-0.4.2 does not support pilot release yet. Protected failures cannot be averaged into the strong citation, security, latency, or partial-cost-estimate results.

Required before reconsideration:

1. Fix duplicate recommendation behavior and prove idempotency.
2. Restore the readback simulator and exercise timeout recovery.
3. Exercise disable and queued-work behavior.
4. Assign independent label authority and adjudicate disputed cases.
5. Bind the production tenant policy to the exact candidate.
6. Add missing-note behavior and relevant non-English adversarial coverage where the target population requires it.

False-green check: intentionally break an evidence citation, replay the same case revision, stale the policy binding, and fail readback; the suite must reject the candidate or prevent a completion claim.

7. Trusted action boundary

The candidate has no submission capability, which is the strongest current safety boundary. Recommendation writes still need tenant and case scope, bounded inputs, an idempotency key, concurrency preconditions, audit fields, and workbench readback. If submission is ever proposed, it requires a separate contract and fresh specialist approval scoped to claim, correction revision, actor, facility, and policy revision.

8. Readiness decision

Decision for rec-0.4.2: hold. The duplicate write, inconclusive recovery path, unexercised rollback/disable behavior, unassigned label authority, and unbound production policy are release-relevant gaps. A corrected exact candidate requires new bound evidence; prior passing results cannot silently transfer.

After revealing S8, review rec-0.4.3 as a distinct candidate. Its bound evidence closes the protected behavior and security failures above, and the Revenue Cycle VP and Product Security Director each record only the disposition within their scope. Mission 8 decision for rec-0.4.3: hold with conditional canary intent. Migration and old-record replay remain unexercised. Safe disable and retreat to the existing specialist-only workflow are valuable evidence, but they are not software rollback and cannot waive those applicable gates.

S8 binds repository commit and build digest, dependency lock, schema and migration, environment, configuration, data context, model/prompt/policy route, capability, and rerun evidence. It also proposes a 20-case daily and 100-case total maximum, an independently adjudicated reference baseline, measurable stop conditions, and safe retreat. Those conditions become a launch decision only after the still-required migration and old-record replay evidence is exercised and independently reviewed.

9. Service review

Before the pilot, S9 records a separate readiness addendum: migration and old-record replay pass for the same exact candidate, and the Platform Lead authorizes only the bounded NorthLake canary. Four-week decision for rec-0.4.3: constrain. Do not scale volume. Citation acceptance is promising, but the small cohort does not establish a correction-error trend. Adoption is 46 percent, duplicate entry consumes most of the time gain, and the unresolved support incident keeps full economics incomplete. Fix the evidence handoff, include the incident cost, and rerun adoption, correction-quality, and cycle-time measurement. The 14.3 percent median improvement also misses the predefined 15 percent median cycle-time continuation floor. The sold 30 percent average-handling-time target remains unevaluable because S1 supplied no metric contract and the two measures are not interchangeable.

10. Transfer and learning

For the constrained rec-0.4.3 service, the receiving team has exercised support, a bounded policy/configuration change, stale-policy response, disable, safe return to the existing specialist-only workflow, and queue reconciliation. Policy revision 9 stales linked evidence; context r8, configuration v3, and the affected security, evaluation, and change records restore currentness for the bounded route. Transfer is not complete: retirement/data disposition remains tabletop, and temporary delivery access is still active. Exit should remain conditional on those exercises or an explicitly accepted residual support boundary.

Sanitized reusable learning: payer-specific inherited adapters need versioned policy exceptions and regression evidence. Do not productize the customer, payer, case, or operator details.

Common weak answers

• Treating the SOW, sponsor statement, observed cases, policy, and test report as equivalent truth.
• Claiming two cases prove the normal frequency of specialist intervention.
• Fixing the adapter and assuming the automatic-submission promise is then allowed.
• Scaling because citation quality is high while adoption and workflow cost miss their thresholds.
• Calling a simulation, document handoff, or model-generated status customer or production authority.