Preventive Medicine for Fraud Mitigation: Using Risk Analytics to Keep the Affordable Care Act - Affordable for Am

1. Preventive Medicine for Fraud Mitigation: Using Risk Analytics to Keep the Affordable Care Act - Affordable for America Peter Budetti, MD, JD Deputy Administrator for Program Integrity Centers for Medicare and Medicaid Services January 17, 2013 The Association of Government Accountants 2013 National Leadership Conference

2. Established Approach New Approach Center for Program Integrity Strategic Direction Pay and Chase Prevention and Detection 1 ‘One Size Fits All’ Risk-Based 2 Legacy Processes Innovation 3 Inward Focused Communications Transparent and Accountable 4 Government Centric Engaged Public & Private Partners 5 Stand Alone PI Programs Coordinated & Integrated 6

3. Twin Pillars

4. Twin Pillars Video

5. National Fraud Prevention Program Application for Enrollment Claim For Payment Claims Processing Provider Enrollment Rules Anomaly Detection Predictive Models Social Network Analysis Fraud Prevention System Automated Provider Screening Integrated Data Repository NGD STARS One PI PECOS FID APS FPS IDR Zone Program Integrity Contractors CPI Analytics Lab

6. Risk Based Screening of Providers • Levels of screening by risk-based categories of providers • Limited: physicians, medical groups, clinics, hospitals • Moderate: physical therapists, CMHCs, outpatient rehabilitation facilities, ambulance providers, currently enrolled DMEPOS and home health agencies • High: prospective (newly enrolling) home health agencies and suppliers of DMEPOS; providers and suppliers who have been reassigned due to a triggering event, such as: • Excluded by the OIG • Subject to a payment suspension • Terminated by Medicaid • Subject to other final adverse actions

7. Provider Screening by Risk Level

8. Fraud Prevention System (FPS) Implemented on June 30, 2011. Monitors 4.5 million claims (all Part A, B, DME) each day using a variety of analytic models. Alerts generated and consolidated around providers and subsequently prioritized based on risk. Results are provided to the Zone Program Integrity Contractor analysts and investigators with views by regions. Results are available to CPI and law enforcement partners in a prioritized national view.

9. Examples of “Models” in Credit Card Fraud Rule • Charge for TV in FL – Cardholder lives in CA • (Unlikely charge) Anomaly Charges for 3 TVs in one day (99% of people buy less than 3 in a single day) 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)

10. Model Types in the Fraud Prevention System Predictive Models Predictive Assessment against known fraud cases Rules Rules to filter fraudulent claims and behaviors Anomaly Detection Detect individual and aggregated abnormal patterns vs. peer group Social Network Analysis Knowledge discovery through associative link analysis • Examples • Individuals in databases that signify significant potential problems • Geodispersion beyond acceptable bounds • Examples • # procedures / provider exceeds norm • Length of stay exceeds the norm • # units per day exceeds the norm • Examples • Beneficiary not likely to be eligible for services • Billing behavior that distinguishes fraudulent providers • Examples • Indicators of connections among multiple providers with elevated risk • Continued involvement of revoked providers Known Patterns Unknown Patterns Complex Patterns Associative Link Patterns Industry: 100% Industry: 50% Industry: 22% Industry: 13% HIGH Rate of False Positives LOW

11. Models Run Simultaneously Risk Score Rule Anomaly Predictive Model Social Network Health Care Claims Trigger FPS Risk Score by a Provider’s Book of Business, Not Individual Claim Investigations Complaints Stolen IDs Information from Enrollment

12. Fraud Prevention System Data Reduction 200,000,000 100,000,000 Data Input Data Reduction Point 100,000 10,000 1,000 Normalization & Filtering Prioritization Datafeed Analysis Presentation Detection Prioritized ASRs ASRs Alerts Enhanced Claims User Rules Rules Rules Rules

13. Fraud Scheme Uncovered FPS LeadA company was flagged as high priority because it hit a new Predictive Model. Investigation FPS Data Confirmed Beneficiary Interviews Confirmed: Beneficiaries were not being provided the services billed. Pattern of services is not covered per Medicare policy 80% Percent of the company’s claims were for highly suspicious services 8 Average number of services per week for each beneficiary, compared with national average of 1

14. Outcomes from Applying Model Administrative Actions Value The company is under prepay review and payment suspension. Paid over $500,000 in previous year. Early Detection: The pattern of behavior was identified quickly for new providers – one provider had only been paid $4,000 at detection Command Center: The case was discussed in the Command Center with CPI and OIG staff. Decisions were made quickly to pursue payment suspension. Model Success: The results of the predictive model confirm a high success rate with the leads.