1. 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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7. Moderator Brad Wiggins UI Integrity Project Coordinator
Employment and Training Administration

8. 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

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

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

11. 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

12. 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

13. 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)

14. …. 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

15. 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

16. 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

17. 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

18. Please enter your questions into the Chat Room!
Question and Answer Period
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19. Steering Committee Agenda
Finalize subcommittee assignments
Discuss subcommittee next steps
Discuss next steps to finalize strategic plan
Center communication plan

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