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AI and analytics may help to reduce health care fraud

In 2016, many people in Tennessee were shocked to hear reports regarding hundreds of millions of dollars in health care fraud. At the time, CNN reported that hundreds of people in America would face charges of Medicare fraud that totaled $900 million. One Florida owner of several clinics faced charges for $8 million in Medicare fraud, while a doctor in Texas also faced charges.

The defendants faced a long list of allegations, including the following:

  •          Conspiracy to commit health care fraud
  •          Violations of anti-kickback statutes
  •          Aggravated identity theft
  •          Money laundering

While most of the allegations involved Florida, Illinois, New York and Michigan, no state remained untouched. Also, home health care agencies were responsible for the bulk of the charges. The U.S. Health and Human Services inspector allegedly stated that home health systems remain a hot spot for abuse, fraud and wasteful practices.

Forbes confirms that while many people believe it is patients who commit health care fraud, more often than not, the defendants are health care providers. It is possible to detect these fraudulent activities by analyzing data regarding transactions as well as consumer complaints. However, there are so many health care providers and so much data related to transactions and grievances that this would take a very long time to accomplish.

Because of this, more organizations now rely on AI systems to analyze data. Researchers also propose an even more proactive system that detects fraud before health care providers can rack up large sums of money. The risk with AI is that there may always be cases of false positives. However, for health care providers who are, in fact, participating in health care fraud schemes, trying to outsmart automated detection systems could spell trouble.

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