HSBlox Uses Machine Learning
Standard Rules based edits are included in RevBlox™ as well
- WEDI/SNIP
- PTP (Procedure to Procedure)
- MUE (Medically Unlikely Edits)
- NCCI (National Correct Coding Initiative)
HSBlox Uses Machine Learning
Standard Rules based edits are included in RevBlox™ as well
Rules-Based, with practice-configurable rules
Continuous, Adaptive, & Aware
Machine learning is continuous and auto-adaptive, uncovering changes prospectively, with complete awareness
Limited to, and relies on human awareness
Multi-Practice
Machine learning provides benefit to all practices of changes learned, detecting issues long before an individual practice can detect it. This learning approach is automatically part of the algorithm’s intelligence and does not require configuration.
Rules are practice-specific and do not benefit from the machine learning that is multi-practice. A given practice is only as good as its human detectors, who will miss or not be aware. If it is noticed, it is then configured by a human, which is also prone to error.
Multi-Specialty
Machine learning handles all specialties without the need for specific human knowledge of a given specialty.
Human configured rules require human knowledge across specialties, so it requires multiple humans.
Real-Time Awareness
Retrospective awareness---and limited to what a human has detected
Multi-Layered
RevBlox™ solution is not limited to Charge-posting accuracy. RevBlox™ is aware of charge-posting, eligibility, payer-provider contract, prior authorization, referral, coordination of benefits
Retrospective awareness---and limited to what a human has detected
Granular
The RevBlox™ solution is granular in its edits:
Charge-posting within the practice
Dashboard & Workflow Routing
Complete workflow for routing of claims and remittance to specific users within the practice (or within a Central Billing Office)
Workflow is single-user centric