Clinical Reviews for Claims Payment Integrity
Expert clinical and coding auditors lead our tech-enabled services. They leverage Alivia’s powerful, configurable Payer Analytics Platform to conduct post-payment reviews, or to flag and pause suspect claims in real-time, requesting medical records from providers for a thorough review. This system is ideal for placing specific providers under pre-payment review following a post-payment fraud investigation or scrutinizing certain claims or service types, on pre- or post-payment basis. Alivia’s team assesses claims to determine the appropriate action – denial, adjustment, or original billing – and post-payment recovery approach.
Focus Areas:
- DRG Coding Accuracy
- Compliance With Coding Guidelines
- Documentation Adequacy
- Medical Necessity
- Quality Management System
- Compliance Title 21-CFR Part 820
- Device Classifications & Listings
- Charge Accuracy
- Itemization & Transparency
Examples of Clinical reviews
Clinical Chart Auditing (Billed Charges/% of Billed)
Comprehensive review and assessment of medical records to ensure accuracy, completeness, compliance with regulations, and quality of care provided to patients.
Diagnosis-Related Group (DRG) Audit
Detailed examination of medical records and coding practices to ensure accurate assignment of DRGs.
FDA Implant Audit
Compliance with regulatory requirements related to medical implant billings.
Itemized Bill Audit
Verification of the accuracy and completeness of charges billed for medical services provided to a patient.
Team of experts
Clinical Auditors RN, LPN Licensed medical review experts
Coding Auditors CCS, CIC, CPC Certified coding review experts
Quickly identified and confirmed fraud and payment issues.”
AI ASSIST FUNCTIONS
Alivia’s AI capabilities extend beyond traditional data analysis, providing predictive modeling, real-time decision support, and automated workflows that enhance operational effectiveness. By leveraging AI, we help organizations avoid fraud, optimize their payment processes, and confidently make data-driven decisions.
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