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Health Payers enterprise team working with Salesforce Health Cloud · AWS Bedrock · FHIR APIs — pronix.ai case study
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.

National health payer, 22M members · Salesforce Health Cloud · AWS Bedrock · FHIR APIs

Client
National health payer, 22M members
Industry
Health Payers
Platform
Salesforce Health Cloud · AWS Bedrock · FHIR APIs
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
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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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