True AI Medical Claim Fraud Investigation
Artificial intelligence in healthcare is designed to focus on cases that previously required a trained human eye and sometimes intuition to detect. The Centaur is a hybrid software system assisting while independently learning from those interactions.
The Centaur cooperates with medical claim fraud investigators and reviewers, leveraging each’s intrinsic strengths to uniquely and effectively find medical fraud. The reviewer transfers knowledge to the Centaur as they investigate cases using their intuition, experience, and domain knowledge, identifying and responding to subtle cues, edge cases, and new indicators of evolving fraud tactics.
As the platform is utilized, the user begins “teaching” the system through their responses to questions posed and interaction between the system and the operator. Over time, artificial intelligence in the Healthcare industry will begin to be increasingly independent of its human partner, eventually taking the lead in the mutually beneficial working relationship.
The Centaur provides experienced, highly trained investigators and analysts a powerful tool that complements and enables them to work “at the top of their license”. The WhiteHatAI Centaur platform revolves around augmenting a reviewer’s capabilities, not replacing the reviewer. It capitalizes on the strengths of Artificial Intelligence while avoiding the risks of purely automated approaches.
Our Centaur artificial intelligence med claim fraud software empowers the skilled reviewer with the ability to concentrate on specific issues instead of spending valuable time on less complicated issues. It maximizes the investigatory workflow by prioritizing outcomes and only bringing the most complicated files and issues to the attention of investigators and reviewers.
As the Centaur brings specific issues inside of medical claims to a reviewer, it learns by observing and capturing exactly how the reviewer addresses those issues. The Centaur learns from these interactions, answering more of complex questions itself, allowing a reviewer much higher volume of claims to be reviewed in the same amount of time.
The sheer number of claims processed in the U.S. is daunting, creating a needle-in-a-haystack scenario for identifying fraud, waste, and abuse. Even with traditional automation and safeguards, erroneous claims can go undetected, easily bypassing administrative edits in most claims adjudication systems.
WhiteHatAI’s Centaur relieves the stress of high claims volume, literally handling hundreds of thousands of claims per hour and referring suspicious activity, files and claims to the appropriate personnel with the same skill and accuracy of a skilled medical claims reviewer. Instead of scaling with trained reviewers, the Centaur provides a scaling option at a fraction of the cost with better auditing results.
THE CENTAUR ADVANTAGES
THE WHITEHATAI CENTAUR SYSTEM PROVIDES A NUMBER OF ADVANTAGES OVER EXISTING METHODS OF DETECTING FRAUDULENT CHARGES IN MEDICAL CLAIMS
- Improved error detection across multiple knowledge areas
- Consistent high-performance metrics regardless of time on task
- No bad habits and/or easy to re-train
- Does not solely rely on human-created rules but instead independently adjusting AI
- Uses AI to drive meaningful understanding of fraud, waste and abuse patterns and trends in a continuous learning mode
- Allow client-owned /pre-existing tools to be easily integrated into the Centaur
- Employs both rules-based and predictive AI for provider profiling
- Applies clinical code edits with specific and unique business rules to reflect and enforce a payer’s contracts and payment policies
- Reduces false positives
- Provides experienced, highly trained investigators and analysts a powerful tool that complements and enables them to work “at the top of their license”
- Maximizes investigatory workflow by prioritizing outcomes by only bringing the most complicated files and issues to the attention of investigators and analysts
- Concentrates on pre-payment or pre-adjudication
- Increases efficiency of investigators