Nebraska LB 77 (Ensuring Transparency in Prior Authorization Act)

Binding law — in force

AI may not be the sole basis to deny, delay or modify care; a physician or clinical peer makes adverse determinations (Nebraska LB 77)

Nebraska LB 77, Sec. 12(1) · official text · In force: applies since 1 Jan 2026 · Nebraska (US-NE)

From 2026-01-01, an artificial intelligence-based algorithm shall not be the sole basis of a utilization review agent's decision to deny, delay or modify health care services based in whole or in part on medical necessity (LB 77 sec. 12(1)), and the agent must ensure that all adverse determinations for prior authorization are made by a physician (or, where the requesting provider is not a physician, a clinical peer of that provider) holding a current nonrestricted U.S. licence, with appropriate training, knowledge or expertise, under the clinical direction of a medical director responsible for Nebraska enrollees (sec. 4(1)). Detect automated output that sets a denial, delay or modification without a physician's decision.

Who it applies to

• Duty falls on: insurer, organization
• Sectors: insurance, healthcare
• Utilization review agents (any person, company, health carrier or other entity performing utilization review, with the 44-5418(31) exclusions) making prior-authorization and other medical-necessity decisions for Nebraska enrollees. Operative 2026-01-01 (LB 77 sec. 17).
• Not covered:
  • Not utilization review agents (Neb. Rev. Stat. 44-5418(31)): federal agencies and agents acting for the federal government or the State (to that extent), agencies of the State, internal quality assurance programs not used to allow or deny claims, licensed pharmacists and pharmacies in the practice of pharmacy, workers' compensation utilization review, individuals employed by a certified agent, and ERISA-exempt employee benefit plans
• Whether it applies depends on facts outside the code; a person has to decide.

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)

Related incidents

No guardrail sits on this exact control; these incidents are cited by guardrails on related controls.

• UnitedHealth nH Predict claim-denial litigation (2023-11; alleged (not proven)). A class action filed in November 2023 alleges that UnitedHealth's nH Predict model had a 90% error rate, measured by denials reversed on appeal, while only about 0.2% of members appealed. UnitedHealth disputes the allegations; the litigation is ongoing. Source: STAT News · evidence grade: primary · cited by Monitor how often adverse AI decisions are reversed, and suspend models that are usually wrong
• Cigna PXDX batch claim denials (reported) (2022; alleged (not proven)). ProPublica, citing internal Cigna records, reported that Cigna's PXDX system was used to reject more than 300,000 claims over two months in 2022, with physicians spending an average of 1.2 seconds on each. Cigna disputes the reporting; related lawsuits are ongoing. Source: ProPublica / The Capitol Forum · evidence grade: press of record · cited by Make human review of adverse AI decisions substantive, not nominal

Rule id ne-lb77.ai-not-sole-basis-physician-decides · review status: primary source derived

Binding law — in force

Disclose AI use in utilization review to the Department, network providers, enrollees and the public website (Nebraska LB 77)

Nebraska LB 77, Sec. 12(2) · official text · In force: applies since 1 Jan 2026 · Nebraska (US-NE)

From 2026-01-01, a utilization review agent must disclose to the Department of Insurance, to each health care provider in its network, to each enrollee, and on its public website if artificial intelligence-based algorithms are used or will be used in the utilization review process (LB 77 sec. 12(2)); the Department may audit its automated utilization management system at any time (sec. 12(3)). Detect the absence of the four disclosures.

Who it applies to

• Duty falls on: insurer, organization
• Sectors: insurance, healthcare
• Utilization review agents (any person, company, health carrier or other entity performing utilization review, with the 44-5418(31) exclusions) making prior-authorization and other medical-necessity decisions for Nebraska enrollees. Operative 2026-01-01 (LB 77 sec. 17).
• Not covered:
  • Not utilization review agents (Neb. Rev. Stat. 44-5418(31)): federal agencies and agents acting for the federal government or the State (to that extent), agencies of the State, internal quality assurance programs not used to allow or deny claims, licensed pharmacists and pharmacies in the practice of pharmacy, workers' compensation utilization review, individuals employed by a certified agent, and ERISA-exempt employee benefit plans
• Whether it applies depends on facts outside the code; a person has to decide.

resp = client.chat.completions.create(model=MODEL, messages=claim_msgs)
claim_decision = parse_decision(resp.choices[0].message.content)
claims.update(claim_id, status=claim_decision)

AI_USE_NOTICE = ('An AI system helped evaluate your claim. '
                 'You can ask us how it was used and request review by a claims adjuster.')
resp = client.chat.completions.create(model=MODEL, messages=claim_msgs)
claim_decision = parse_decision(resp.choices[0].message.content)
claims.update(claim_id, status=claim_decision, ais_program_ref='AIS-012')
send_ai_notice(claimant, text=AI_USE_NOTICE)

Standards that recommend the same control

• Insurers adopting the NAIC model guidance should maintain an AIS Program and AI-use notice (NAIC AI Model Bulletin · NAIC Model Bulletin, AIS Program Guidelines 1.1-1.9)

Rule id ne-lb77.ai-use-disclosure · review status: primary source derived