IBM Predictive Analytics Helps North Carolina Detect Medicaid Fraud
The New Reality for Customer Engagement
Date: 5/31/2018 @ 1 p.m. ET
North Carolina's Department of Health and Human Services (DHHS) has conducted an investigation of Medicaid fraud by using IBM predictive-analytics software similar to that of IBM's Watson supercomputer.
For its fraud detection, the state is using two IBM applications: IBM Fraud and Abuse Management System (FAMS) and IBM InfoSphere Identity Insight to analyze claims of nearly 2 million Medicaid patients and 60,000 providers to detect suspicious billing patterns.
FAMS mines data to detect patterns of fraud and abuse using modules configured for North Carolina Medicaid. "The algorithms and models are used across the health care industry (both public and private payers) as well as cell phone companies, property and casualty insurers," Shaun Barry, IBM's fraud and abuse leader, wrote in an email to eWEEK.
The program can detect billing behaviors that suggest fraudulent activity.
"The beauty of this system is that it recognizes patterns of billing behavior that don't fit in with the norm and then takes it a step further by looking for relationships among providers that can point us to a Web of suspicious accounts," Al Delia, North Carolina DHHS acting secretary, wrote in an IBM blog post.
Meanwhile, InfoSphere Identity Insight allows organizations to resolve identity conflicts and determine if providers are using different names on billing statements, said Barry.
"It resolves many distinct provider numbers into single entities based on shared attributes, characteristics and numbers," Barry explained.
DHHS announced the results of its Medicaid fraud research on May 22.
North Carolina Governor Beverly Purdue announced in 2010 that the state would use IBM software to analyze medical claims.
FAMS and InfoSphere offer machine-learning capabilities similar to Watson, according to Barry. The more data fed into the software, the smarter it becomes, he said.
"What Watson did on "Jeopardy" is it learned to understand the categories and their context as it played," said Barry. "This helped it build a better profile of answers for the category."
North Carolina's fraud detection is following a similar process, he said.
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