AI solutions company, Pearl, received a patent grant for its AI-powered FWA detection system. The FWA- fraud, waste, and abuse- the system uses AI to search for indications of radiographic claims identical to other claims. The patent was awarded by the US Patent Office; the grant marks the company's fourth patent in 2021, each of which has been related to machine learning.
Carriers of dental insurance lose about $12.5 billion annually to fraudulent claims. The amount squanders about 5% of insurers' overall dental care spending, which is $250 billion annually. But fraud may go unchecked because there is a multitude of claims, claims examiners have large amounts of work, and fraudulent claims that use radiographic images may be difficult to tell apart from authentic claims. Fraudulent radiographic claims often utilize an image that has been submitted in the past.
"With resubmission fraud, the radiographic claims evidence is real, just from a previously approved claim or a different patient, Ophir Tanz, CEO of Pearl, said. "An examiner has no way of knowing its FWA unless they remember seeing the evidence before. Considering the hundreds of carriers and billions of x-ray attachments, there's a next-to-zero chance an examiner will have seen that radiograph––let alone remember seeing it. This makes FWA detection a problem of recall and information sharing as well as of examiner resources. Machine learning is the only viable means of tackling all three. We felt an obligation to patients, carriers, and the majority of dental care providers unfairly tarnished by suspicions over the actions of a few rogue dentists to apply our machine learning expertise toward engineering a viable FWA abatement technology."
Pearl's AI technology uses computer vision algorithms to review a large number of images and machine learning to identify duplicate radiographic images among the volume of data.
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