Predictive claims scrubbing trained on millions of historical claims.
Machine learning scrubbers trained across millions of historical claims that detect unbundled codes, modifier mismatches, and medical policy nuances before submission. We stop claim denials before they can happen.
Post-Submission Clearinghouse Rejection Rate
How ARDA Health-Tech Delivers Operational Precision
A disciplined blend of autonomous software and clinical specialists executing with zero tolerance for administrative error.
Historical Claim Pattern Analysis
Deep learning models analyze historical adjudication trends across specific payer IDs to predict denial probability.
Dynamic Rules & Policy Engines
Daily automated updates incorporating Local Coverage Determinations (LCD) and National Coverage Determinations (NCD).
Intelligent Modifier Optimization
Automated detection of missing modifiers (25, 59, XE, XP) backed by clinical chart evidence.
Measurable Revenue & Efficiency Gains
Clear operational deliverables backed by strict Service Level Agreements (SLAs).
- ✓ Initial clearinghouse rejection rate driven down to under 1.2% across all electronic batches
- ✓ Elimination of post-adjudication takebacks caused by unbundled procedural billing
- ✓ Immediate visual flags highlighting high-risk claim lines for human coder review prior to filing
- ✓ Continuous model retraining incorporating every payer adjudication response
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