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Case study · Health Payers · Agentic AI

82% prior-authorization touchless rate at a national payer — clinical-safe AI automation

Prior authorization was a 6-day process that cost the payer $92M annually and drove providers to public complaint. pronix.ai automated the clinical-review workflow with clinician-in-the-loop — 82% touchless approvals, 4-hour turnaround and full CMS interoperability rule alignment.

Client
National health payer, 22M members
Industry
Health Payers
Platform
Salesforce Health Cloud · AWS Bedrock · FHIR APIs
By pronix.ai Strategy Practice8 min readQ1 2026
82%
Touchless approval rate
6d → 4h
Average PA turnaround
$62M
Annualized administrative savings
0
CMS interoperability findings

*Representative outcome; results vary by client, scope and platform configuration.

The challenge

88 million PA requests per year across 340 service categories, each touching 4-6 humans. Providers publicly named the payer as the slowest in the region, and CMS interoperability rules loomed.

Our approach

Step 01

Guideline-grounded review agent

Every request evaluated against the payer's published medical policy — approvals cited the exact criteria met.

Step 02

Clinician-in-the-loop for denials

The agent never denied autonomously — every proposed denial routed to a physician reviewer with a pre-built evidence pack.

Step 03

FHIR-native provider surface

Providers submitted via FHIR PA APIs and received a decision or a specific-missing-information response within hours.

Step 04

CMS-aligned audit trail

Full decision trail exportable in the CMS-required format — regulator-ready by construction.

Step 05

Continuous policy alignment

Medical policy changes flowed to the agent within 24 hours with a re-evaluation harness — no drift between paper policy and shipped decision.

The right answer for PA is faster yeses, better nos, and every no a physician made. That is exactly what pronix.ai built.

Chief Medical Officer
For Chief Medical OfficerFor VP Utilization ManagementFor Chief Digital Officer

Illustrative case study. Scenarios, metrics, quotes and client details are representative composites based on Pronix engagements and industry benchmarks unless a named client is shown with written consent. Outcomes vary by client, scope, data quality and platform configuration. Nothing on this page is a guarantee, warranty or professional advice. See our Terms of Use for the full disclaimer.

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