Use case 1 · AI prior authorization at scale
CMS-0057-F mandates faster prior auth turnaround for Medicare Advantage, Medicaid, and ACA plans starting 2026. AI-drafted determinations cut turnaround 62% and improve provider satisfaction.
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AI Engineering for Health Insurance Payers
AI prior authorization, claims AI, denial management, risk adjustment, member engagement, and utilization management for commercial, Medicare Advantage, and Medicaid payers. Built by the team that’s been integrating with healthcare systems since 2013.
$45K Discovery · $95K Production-Ready · $145K Pilot-Ready
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📌 Definition
Payer AI is the engineering of AI features for health insurance organizations — AI prior authorization, claims AI, denial management, risk adjustment, member engagement, utilization management, and provider network analytics. Modern 2026 payer AI deployments require BAA-eligible inference paths, NCQA-aligned utilization management workflows, CMS-compliant prior auth turnaround times, HEDIS quality measure infrastructure, and integration with claims platforms (Facets, QNXT, HealthEdge) and provider EHRs via FHIR. Productized fixed-price tiers: $45K (4-week Discovery), $95K (8-week Production-Ready), $145K (12-week Pilot-Ready).
What we build for payers
- 01
AI prior authorization
AI-drafted prior auth determinations from clinical evidence. 62% faster turnaround. CMS-compliant timelines (72-hour standard, 24-hour expedited). Reviewer-in-the-loop architecture.
- 02
Claims AI
AI-driven claims adjudication assist, code review, medical necessity determination, and edit logic. Operates inside Facets, QNXT, HealthEdge, or proprietary claims platforms.
- 03
Denial management AI
AI-drafted denial letters with clinical justification. AI-drafted appeal responses when denials are appealed. Predicts denial likelihood pre-adjudication.
- 04
Risk adjustment AI
AI-driven HCC (Hierarchical Condition Category) coding from chart data for Medicare Advantage and ACA risk adjustment. RADV audit-ready documentation. Reviewer-in-the-loop.
- 05
Member engagement AI
AI symptom triage, AI scheduling, multilingual member chat, plain-language EOB explanation, AI-driven care gap closure outreach.
- 06
Utilization management AI
AI-assisted concurrent review, retrospective review, and case management workflows. NCQA-aligned documentation. InterQual and Milliman Care Guidelines integration.
- 07
Provider network analytics
AI-driven provider performance benchmarking, tiering analytics, narrow-network design support, value-based contract performance tracking.
- 08
HEDIS quality measure AI
AI-driven HEDIS measure capture from chart data. Care gap identification. Member outreach prioritization. NCQA audit-ready documentation.
- 09
Population health AI
AI-driven risk stratification of attributed lives. Care gap closure prioritization. SDoH-aware outreach targeting. ACO and value-based contract support.
Payer AI pricing · 2026
What’s included in every payer engagement
- HIPAA-compliant architecture from day 1 — encryption at rest and in transit, role-based access, audit logging
- BAA-covered AI providers (OpenAI, Anthropic, AWS Bedrock, Google)
- NCQA-aligned utilization management documentation
- CMS-compliant prior auth turnaround time tracking
- HEDIS measure capture infrastructure
- Claims platform integration (Facets, QNXT, HealthEdge, proprietary)
- Provider EHR integration via FHIR R4 (Epic, Cerner-Oracle, Athena, Allscripts)
- InterQual and Milliman Care Guidelines integration
- CMS-0057-F (Interoperability and Prior Authorization Final Rule) compliance patterns
- RADV audit-ready risk adjustment documentation
- Pre-signed BAA templates with major cloud and AI providers
- SOC 2 Type II and HITRUST audit-ready documentation
Common payer AI use cases
Use case 2 · Risk adjustment AI for Medicare Advantage
HCC coding from chart data. RADV audit-ready documentation. Reviewer-in-the-loop. Improves risk score accuracy without RADV exposure.
Use case 3 · Denial management AI
Predicts denial likelihood pre-adjudication. AI-drafted denial letters with clinical justification. AI-drafted appeal responses. Cuts manual denial review workload 40–60%.
Use case 4 · Member engagement AI
AI symptom triage, multilingual chat, plain-language EOB explanation, AI-driven care gap closure outreach. Lifts member engagement scores and reduces call center volume.
Use case 5 · HEDIS measure AI
AI-driven HEDIS measure capture from provider chart data. Care gap identification. Member outreach prioritization. NCQA audit-ready documentation.
Production reality
Ship payer AI that survives NCQA, CMS, and RADV audits
Free 30-min architecture call. We’ll scope your AI use case, regulatory requirements, and the right tier for your line of business.
FAQs
FAQ
CMS-0057-F mandates 72-hour standard and 24-hour expedited prior auth turnaround for Medicare Advantage, Medicaid, and ACA Marketplace plans starting January 1, 2026. AI-drafted determinations cut turnaround to hours instead of days while maintaining clinical judgment as the binding constraint (reviewer-in-the-loop). Every Taction prior auth engagement is CMS-0057-F-aligned by default.
Yes — when properly architected. The AI suggests HCC codes from chart documentation; a certified coder reviews and signs. Every coding decision has a chart citation. RADV audit-ready documentation is generated automatically. Our $30K RADV audit support add-on covers the documentation and chart review workflows auditors expect.
Yes. We’ve integrated with Facets, QNXT, HealthEdge, and proprietary claims platforms. Integration patterns: REST API where supported, ESB/MFT for legacy platforms, RPA wrappers for systems without modern APIs. We scope integration architecture per engagement.
Yes. Our AI features integrate with existing utilization management workflows — InterQual, Milliman Care Guidelines, proprietary review criteria. The AI surfaces evidence and drafts determinations; the UM nurse or medical director makes the final call. NCQA-aligned documentation throughout.
Same as our hospital engagements. Pre-signed BAA templates with OpenAI, Anthropic, AWS Bedrock, and Google. Zero-data-retention configuration verified in writing. Audit logging on every model output. Some payer engagements use on-prem LLM deployment when data sovereignty requires it.
AI-driven HEDIS measure capture from provider chart data is one of the highest-volume payer AI use cases in 2026. The AI identifies care gaps and supplemental data from chart text; HEDIS-certified abstractors review and confirm. NCQA audit-ready documentation throughout.
4-week Discovery, 8-week Production-Ready, 12-week Pilot-Ready. Full production with claims platform integration adds 16–32 weeks. NCQA-accredited workflows add validation time per the accreditation calendar.