UI Integrity Center of Excellence: Centers for Medicare & Medicaid Services Briefing and Steering Committee Meeting February 4, 2013 11:00 a.m. ET

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Moderator Brad Wiggins UI Integrity Project Coordinator Employment and Training Administration

Featured Speakers Presenters Moderator Kelly Gent, Center for Program Integrity, U.S. Department of Health and Human Services Moderator Brad Wiggins, UI Integrity Project Coordinator, Employment and Training Administration

Objectives Information about HHS-CMS fraud center Understanding data analytics/predictive modeling solution Q&A to inform UI Center activities

Predictive Analytics Kelly Gent Director Data Analytics and Control Group Center for Program Integrity Centers for Medicare & Medicaid Services

Fraud Prevention System The Small Business Jobs Act of 2010 required that CMS use predictive analytics and other sophisticated technology to prevent fraud, waste, and abuse in the Medicare Fee-for-Service Program. As cited in the First Year Report to Congress: Prevented or identified $115.4 million in payments Generated leads for 536 new investigations and augmented information for 511 pre-existing investigations Implemented FPS nationwide ahead of legislative requirements Implemented 37 new or significantly improved models and 6 significant enhancements

Fraud Prevention System Monitors 4.5 million Medicare claims each day Alerts generated and consolidated around providers and subsequently prioritized based on risk. Regional results are provided to the Zone Program Integrity Contractor analysts and investigators. Results are available to CPI and law enforcement partners in a prioritized, national view. Zone Program Integrity Contractors work the top 100 leads in each zone

Examples of “Models” in Credit Card Fraud Rule Charge for TV in FL – Cardholder lives in CA (Unlikely charge) Claim for a senior whose ID was stolen # doctors a senior sees in a month Anomaly Charges for 3 TVs in one day (99% of people buy fewer than 3 in a single day) Ambulance model – we know which characteristics matter – will show in FPS Link together submitters – moving towards more sophisticated linking based on characteristics Predictive Model Charges for multiple TVs out of state, after a $1.00 charge, on Wednesdays after midnight (Based on experience, these charges have a very high probability of being bad) Social Network Charge for a TV at an address known to have bad charges using a card with a phone number used by a known bad actor (relationship suggests a problem)

…. And Other Information Fraud Prevention System New Approach Rule Anomaly Predictive Model Social Network Health Care Claims (Credit Card Charges) …. And Other Information Providers (Cardholders) Book of Business Fraud Prevention System

First Year of the Fraud Prevention System Built the FPS foundation and infrastructure Established governance structure to prioritize models and enhancements Provided access and training Created an Analytics Lab and built data analysis expertise through internal and external partners Established collaboration and partnership as a standard business practice Solicited and incorporated user feedback

What is the Command Center? The Command Center is the center for excellence for detection and investigation, driving program integrity innovation and improvement. The Command Center is a concept – a paradigm shift. Introduces the “mission” – bringing experts together to solve specific program integrity problems Speed up actions by changing the approach from linear to point-in-time collaboration

Command Center Missions Analytics Investigative Operational Ad Hoc Purpose Develop effective new models for inclusion in the FPS or APS Purpose Establish standard, innovative investigative approaches to resolving suspect providers Purpose Work specific suspect providers in real-time and take appropriate administrative action Purpose Provide training or conduct meetings that require a collaborative environment Example Model Development Workgroup Collaborate to build effective model to identify entities with characteristics correlated with fraud Example Investigative Approach Bring together multi-disciplinary team to identify new approaches for investigating a type of provider; Identify new policy and data implications Example Regional Fraud Collaborative Collaborative across CPI, ZPICs, State, OIG, DOJ, FBI to work high priority leads Example FPS Pre-Release Training Train representatives from each user group on new models and functionalities in upcoming releases

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Steering Committee Agenda Finalize subcommittee assignments Discuss subcommittee next steps Discuss next steps to finalize strategic plan Center communication plan

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