Ammar Qazi's Contact Details

Ammar Qazi's Company Details

FraudLens

Arlington, VA, US • 20 - 49 Employees
Healthcare Facility/Healthcare Services

FraudLens (FL) is a one-of-a-kind software solution that enables healthcare payers to finally make a huge impact on fraud, waste, and abuse (FWA) by moving from a "pay and chase" and "manual review" model to an automated "detect and prevent" model. FL's complete solution that includes a near real-time FWA detection system and a Provider Relationship Platform (PRM), after many years of research and development, is specifically designed to address the financial burden caused by pre-payment of high volume, low dollar FWA as well as the low volume, high dollar FWA that exist within the healthcare industry today. It is estimated that 90% of all FWA is low dollar claims that are not being addressed today.FraudLens' sophisticated near real-time FWA detection system is developed through a combination of the following attributes:Clinical KnowledgeInformaticsForensicsInformation TechnologyAdvanced Data VisualizationUsing this unique combination, FL has developed a multi-vectored (multiple angles/scenarios/algorithms) approach that reviews all claims from all providers within hours and detects and provides the exact details of Who, What, When, Where, and How FWA is committed. This unique multi-vectored approach is administered pre-payment and pre-adjudication saving the healthcare payer millions of dollars.The FraudLens FWA findings (reports) are then fed real-time into FL's innovated Provider Relationship Management platform. The PRM is used to achieve a behavioral change in the providers through communication, education and incentives used to entice the provider to stop submitting questionable claims. PRM tracks the FWA over time, identifies any changes in provider behavior and triggers an escalation of communication between the Provider and Payer. The end result is that FraudLens provides a complete solution that changes the current "pay and chase" and "manual review" model to an automated "detect and prevent" model.