AI or algorithm denies, delays or downgrades care in utilization review without a licensed clinical reviewer deciding

Route every adverse outcome an AI or algorithm proposes in utilization review to a qualified clinical reviewer, and issue a denial only from that reviewer's recorded decision.

At the point where a model, rules engine or scoring tool returns its result for a prior-authorization, concurrent or retrospective review, the code may auto-approve (where the law allows) or route the case, but any result that would deny, delay, modify or downgrade the request is written as a pending clinical review (status 'pending_clinical_review', a review_queue entry with the tool's output attached as a recommendation), never as the determination. Only a review action by an authenticated reviewer whose role is physician, clinical peer or qualified reviewer, in the same or a similar specialty where the law requires, can set an adverse status; that action records reviewer_id, licence and specialty, the clinical documents opened, the decision and its clinical rationale, and the timestamp, and the adverse-determination notice is generated from it (with the reviewer's signature or attestation where the law requires). Where a law forbids the automated system from making an adverse determination even in part (Texas), the tool's output may only approve, route or support administrative and fraud-detection work; it is not shown to the reviewer as a proposed denial.

Where it goes: 1 application source code, 2 data models, 9 AI output handling, 14 user-facing text.

What reviewers look for: no code path that sets a denied, declined or downgraded status, or sends a denial letter, directly from model or scoring output; a pending_clinical_review or review_queue step on every adverse path; a reviewer decision record (reviewer_id, role, specialty, documents reviewed, rationale) required by the endpoint that issues the adverse determination; notices built from that record and signed where required; and, for Texas, no model prompt or output schema that asks the tool to return a denial.

result = client.chat.completions.create(model=MODEL, messages=build_pa_prompt(request)).choices[0].message.content
if json.loads(result)['decision'] == 'deny':
    prior_auth.update(request.id, status='denied')
    send_denial_letter(request)

result = json.loads(client.chat.completions.create(
    model=MODEL, messages=build_pa_prompt(request, record=member_clinical_record(request))).choices[0].message.content)
if result['decision'] == 'approve' and AUTO_APPROVE_ALLOWED:
    prior_auth.update(request.id, status='approved', ai_assisted=True)
else:                                   # any non-approval goes to a clinician
    review_queue.enqueue(request.id, queue='pending_clinical_review',
                         specialty=request.specialty, ai_recommendation=result)

@app.post('/reviews/{case_id}/decision')
def record_clinical_decision(case_id: str, body: Decision, reviewer=Depends(licensed_clinical_reviewer)):
    decision = clinical_decisions.create(case_id=case_id, reviewer_id=reviewer.id, licence=reviewer.licence,
                                         specialty=reviewer.specialty, documents_reviewed=body.documents,
                                         outcome=body.outcome, rationale=body.rationale)
    if body.outcome in ('denied', 'downgraded'):
        send_adverse_determination(case_id, decision=decision, signed_by=reviewer)

Engineering guidance, not legal advice. Each provision below may add its own details (a cadence, a deadline, a required notice element): open it for those.