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GCN : April 2014
CASE STUDY HEALTH IT Massachusetts is using predictive modeling to combat fraud in Medicaid payments, saving the Commonwealth from shelling out millions of dollars in false claims to health care pro- viders. The predictive analytics application sup- ports Massachusetts' Executive Office of Health and Human Services fraud investi- gations by providing real-time risk assess- ment on health claims. By shifting away from a "pay-and-chase" model, investigators have been able to re- cover $2 million in improper payments and have avoided paying hundreds of thou- sands of dollars in fraudulent claims dur- ing the first six months of operations, said Joan Senatore, director of the Massachu- setts Medicaid Fraud Unit. The system went live on May 27, 2013, with a pre-payment screening for claims that helps protect MassHealth from unnec- essary expenditures and creates a more ac- curate reimbursement for billing providers, Senatore said. Almost every state uses a pay-and-chase method for processing Medicaid and Medi- care claims. After paying a claim, state investigators examine claims randomly, search for fraud patterns on their own or get tips from whistleblowers. If they detect fraud or waste, they have to prove fraud has been committed. It's an inefficient process in which states often do not get back all of the money paid out in bad claims, said Kathy Baird, vice president and general manager of state and local programs with Dynamic Research Corp. (now a part of Engility), the systems integrator leading the predictive analytics project. Most states have tools for analyzing claims that work with back-end IT systems, and MassHealth still uses those types of tools for post-payment analysis, Senatore explained. But state officials wanted to identify anomalies and patterns much ear- lier in the process, she said. To better manage these cases and ensure compliance by providers, MassHealth used BAE Systems' NetReveal (formerly known as Detica NetReveal) fraud detection soft- ware that incorporates predictive analytics and social networking analysis to identify claims that could lead to improper pay- ments. NetReveal integrates with Massachusetts' Medicaid Management Information System (MMIS) and analyzes public and other Com- monwealth data stored in a data warehouse, automatically assessing risk scores associ- ated with claims, Senatore said. Investigators always had access to data- bases and other data sources, but now "we bring all data into one area," she said, such as the master death file and the Office of Inspector General's provider exclusion list. "We download all files into the predic- tive model and can identify if someone has died or if a provider has been excluded by Medicaid and Medicare," Senatore ex- plained. "These are things we do check on at the back end, but now we are checking them much sooner." Within seconds after a claim passes through MMIS for verification, the predic- tive analytics system takes it and applies models to detect any anomalies. A claim can be denied right away, put on hold while investigators do further analysis or paid while the investigation is ongoing, DRC's Baird said. CHOOSE DATA CAREFULLY To ensure that predictive models can ac- curately identify patterns and relation- ships between people and data, analysts must carefully choose which data sources to start with, Baird said. They should start out with small data sets and build analytics around them. Then, build models that can look for overlapping information. NetReveal's origins were in fraud de- tection for the financial sector. As a re- sult, specialists who understood medical claims had to create detection models while technology experts configured the models for the IT systems, Baird explained. MassHealth officials also chose to keep the predictive analytics system within its own IT infrastructure along with MMIS rather than outsource anything to a cloud com- puting environment, she noted. MassHealth officials will continue to build on the system. "We are still in the in- fancy stage now," Senatore said. "We keep adding new detection scenarios identified through the system" as well as scenarios suggested by investigators and analysts, she said. The analytics system is not a "be-all" tool for fraud, but it cuts down on time to ana- lyze claims. "Hopefully, it will send a mes- sage to the provider community that we are looking right away, and that will deter the fraudulent claims coming in," Senatore said. • Predictive fraud detection software helped Massachusetts recover $2 million in improper payments How MassHealth cut fraud with analytics BY RUTRELL YASIN 32 GCN APRIL 2014 • GCN.COM