Big Data and AI: The Weapon of Choice to Fight Healthcare Fraud
It is an undisputed reality that the healthcare industry loses tons of money to the fraudulent cases and health care abuse. From billing for services, supplies or equipment that were not provided to billing for services not medically necessary but performed to increase reimbursement to accepting kickbacks for patient referrals, health care abuse involves everyone, the patients, medical professionals as well as the contractors. Insurance fraud considered as misrepresenting facts or being deliberately dishonest to receive more from an insurance payout than standard costs a fortune to the healthcare sector too.
Traditionally, anti-fraud efforts focused on detection after finding the fraudulent activity and stopping it before any further loss occurs. But with the powerful tools like big data and AI in the marketplace, they allow more accurate analytical data management and pattern recognition, reducing the false positives, and preventing frauds. Big data provides access to more information, and on a much more real-time basis. Applying AI to that data gives the ability to use machines to create more advanced and accurate analytics that too in a faster way, allowing companies to evaluate suspect claims before the payment processing.
In the continually evolving healthcare security landscapes and compliance requirements, big data gives the extra edge, allowing organizations to combine, integrate, and analyze all of the data at once—regardless of source, size, or the format, and AI effectively identifies patterns needed to address health care fraud challenges. With the advent of big data analytics alongside the implementation of AI in the healthcare sector, prevention has got stronger than detection, placing it in a vital place in the vanguard of the fight against health care fraud.
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