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GCN : July 2015
GCN JULY 2015 • GCN.COM 27 Therefore, using prepayment analyt- ics requires the ability to fine-tune the risk equation by balancing the require- ments of payers and providers. “You need a soft touch,” which might in- volve putting a test in place and track- ing its impact over several months, Faidley said. LexisNexis recently helped New York City’s Human Resources Administra- tion use predictive analytics to study costs related to providing benefits to its 2.9 million Medicaid recipients, 1.8 million Supplemental Nutrition Assis- tance Program recipients and 350,000 Cash Assistance recipients. The company combined the agency’s data with LexisNexis’ public records data, “which gives us broad context of information about each beneficiary,” Faidley said. By applying its analytics scoring model to differentiate low-risk beneficiaries from those who “definite- ly need to be investigated,” the city was able to flag costs associated with 9,700 cases and save more than $52 million. THE MOVE TO ZERO TRUST Predictive analytics allow organiza- tions to broaden their searches for pat- terns of fraud. In addition, new iden- tity security tools are emerging to help keep tabs on enterprise players who might be victimized in fraud schemes. “These are privileged users with ac- cess to everything in the database — not just their records; they have the ability to go from system to system inside a corporate or government in- frastructure,” said Ken Ammon, chief strategy officer at Xceedium. Those users have been at the center of several recent high-profile attacks. Their privileges were exploited as the result of sophisticated spear-phishing attacks, including the one on health in- surer Anthem earlier this year in which The Fraud Prevention System (FPS) is a nearly four-year effort by the Centers for Medicare and Medicaid Services to help automate the review of health care claims before, during and after they are filed. CMS considers FPS a power tool in its plan to move away from “pay and chase” to a prevention model of claims management in its fight against fraud. In the past, CMS typically paid a claim then checked its validity before making a decision to try to recover the funds if it discovered they had been paid improperly. That approach has made it easy for fraudsters to elude regulators by sending up a smoke screen of false claims and counter-claims during the payment process. FPS uses predictive analytics to flag providers and other players in the health care supply chain who might have participated in payment fraud. As in anti-fraud approaches in the credit card industry, FPS enables CMS to assign risk scores to specific claims and providers, thereby establishing a starting point for analysts to pursue a potential fraud case. When FPS identifies irregular activity, it automatically generates potential investigative leads for program integrity contractors — the teams of experts and data scientists who can help identify actions that can be taken immediately, such as suspending payment or launching a case review. CMS officials say the success of FPS often depends on quickly detecting fraudulent payments, a goal for which it is enhancing some of its response systems. Responding to a suggestion by the Government Accountability Office, CMS has improved the integration of FPS with its claims-processing system, giving FPS the ability to stop payment of improper claims by transmitting a claim denial message directly to the payment system. “ What this means is that FPS can identify billing patterns and claim aberrancies that would be undetectable or difficult to detect by CMS’ current claim edit modules or a single contractor reviewing on a claim-by -claim basis,” said Shantanu Agrawal, director of the CMS Center for Program Integrity, during a hearing held earlier this year by the House Ways and Means Committee’s Oversight Subcommittee. Industry players say FPS shows promise but is still young. Louis Saccoccio, CEO of the National Health Care Anti-Fraud Association, told the subcommittee that “it will take time to effectively refine and adjust the models for such a large and complex system as Medicare in order to realize the full potential that these powerful technologies offer.” — Paul McCloskey CMS’ ANTI-FRAUD POWER TOOL “Integration within the data warehouse is absolutely vital because it lets you visualize things you would not see with smaller datasets.” ELIZABETH SNAVELY, GENERAL DYNAMICS IT 0715gcn_024-028.indd 27 6/30/15 2:30 PM