How AI-Powered VanaaEV™ Turned Revenue Leakage into Revenue Growth
150-provider behavioural health clinic
20,000 patients/month
Fragmented RCM execution
Errors in manual work

75% → 91%
clean claim rate
99%
eligibility accuracy with VanaaEV
~15%
revenue increase
Executive Summary
Client Profile
- 150-provider behavioural health clinic
- 20,000 patients/month
- Fragmented RCM execution
- Errors in manual work
A 150-provider behavioural health clinic moved from fragmented RCM execution to a prevention-first model, root-cause analysis, VanaaEV (VANAA’s in-house AI eligibility and claim-readiness tool), and a 25-member expert team. Clean claims improved from 75% to 91%, reimbursement moved above 90%, eligibility accuracy reached 99%, and revenue increased by approximately 15%.
The Problem
- Reimbursement rate stuck around 75%, driven by eligibility gaps and credentialing issues
- Unresolved aged claims with no structured follow-up workflow
- Poor communication from the previous RCM provider, no transparent reporting or ownership
The Solution
AI-augmented tooling, human-led execution, in the order the work happened.
01
Root-cause analysis
- Rapid analysis identified the primary leakage points: eligibility and credentialing failures, missing payer authorizations, and no systematic follow-up on aged claims
02
VanaaEV, AI-powered, in-house
- Deployed the proprietary AI eligibility and claim-readiness engine, integrated with clearinghouses for bulk uploads
- Single/multi-check workflows, automated payer-rule and credential verification, real-time eligibility, and payer-response learning
- Reduced manual intervention by ~90% and raised pre-submission accuracy to 95%
03
Human execution
- 25 dedicated RCM specialists managing exceptions, appeals, credentialing, and client communication
- Continuous feedback loop: denials and payer patterns feed VanaaEV, improving future prevention
Measurable Impact
Business Impact
Prevention-first RCM turned leakage into growth: meaningful additional annual collections from improved reimbursement, materially lower rework and manual follow-up costs, and recovery of long-aged claims.