LOS ANGELES, August 11, 2021 /PRNewswire/ — pearl, the leader in AI solutions for dental care efficiency, accuracy and consistency, today announced that it has received a patent grant from the United States Patent Office covering the system of company pioneered AI to detect fraud, waste and abuse (FWA) in dental insurance claims. The patent – US Patent No. 62/962,828 titled “Systems and Methods for Detecting Insurance Fraud” – is the fourth granted to Pearl this year for machine learning innovations developed to elevate the dental care industry.
Pearl’s patented system, which provides the technological foundation for its Pearl Protect solution, helps overcome what has long been an obstacle for dental insurers. Every year, insurers lose approximately $12.5 billion — 5 percent of the $250 billion that the insurance industry spends on dental care – due to fraud and other irregularities in dental claims. Until Pearl introduced a computer vision-based solution to this problem, operators had to rely on imprecise statistical analyzes and heuristic processes to limit FWA. The patent announced today covers a new AI process that provides a much more comprehensive and high-precision defense against FWA.
The challenge of fighting AOF is multi-faceted. With billions of claims submitted annually, the problem of reviewer resources arises: given too few reviewers to review each claim, a large portion of the FWA will pass unreviewed. But the problem goes beyond reviewer resources. Even when an instance of FWA lands on an examiner’s desk, there is little chance of it being detected.
“With resubmission fraud, X-ray proof of claims is real, just from a previously approved claim or a different patient, Pearl CEO Ophir Tanz Explain. “A reviewer has no way of knowing it’s FWA unless he remembers seeing the evidence before. Given the hundreds of wearers and billions of x-ray paraphernalia, he There’s almost zero chance that an examiner has seen this x-ray – let alone remember seeing it. This makes detecting FWA a problem of recall and information sharing as well as resources for examiners. machine learning is the only viable way to tackle all three.We felt an obligation to patients, wearers and the majority of dental providers unfairly tarnished by suspicion over the actions of a few rogue dentists to apply our expertise in machine learning to the design of viable FWA reduction technology.”
The machine learning solution developed by Pearl significantly reduces carriers’ vulnerability to FWA by automatically displaying claims containing falsified and duplicate proofs of claim. The patented system deploys a variety of algorithmic computer vision models to accomplish this feat.
“This patent validates the exceptional work of our machine learning engineers in overcoming a challenge quite distinct from the challenges they have mastered with our radiological detection systems,” said Cambron Carter, CTO of Pearl. “It’s one thing to teach a machine learning system to search for a finite set of relevant known quantities – decay, bone loss, etc. It’s quite another to teach a system to search for a unlimited set of unknown quantities. The latter is a big data problem of the kind that only an unsupervised learning AI system developed by experts can solve. It’s exactly the kind of system covered by Pearl’s patent and we are proud that the originality of our invention is recognized.
Over the past 6 months, Pearl has made major breakthroughs in advancing AI and partnerships to support and elevate all facets of dentistry. These breakthroughs have been codified with an assortment of patents granted this year for technologies developed to improve the restoration manufacturing workflowattend radiological pathology detectionand automate insurance claims settlement.
About the pearl
Pearl is shaping the future of dental care by providing AI and computer vision solutions that improve efficiency, accuracy, transparency and patient care. Founded in 2019 by Ophir Tanz, Pearl is backed by Craft Ventures and other leading venture capital firms. For more information or to request a demo, go to http://www.helloearl.com.
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