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
*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
Guideline-grounded review agent
Every request evaluated against the payer's published medical policy — approvals cited the exact criteria met.
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.
FHIR-native provider surface
Providers submitted via FHIR PA APIs and received a decision or a specific-missing-information response within hours.
CMS-aligned audit trail
Full decision trail exportable in the CMS-required format — regulator-ready by construction.
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.”
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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