A VIEW FROM THE OIG: IDATA MINING IN HEALTH CARE COMPLIANCE AND REGULATION: THE NEXT PHASE IN HEALTH PROGRAM INTEGRITY

1. A VIEW FROM THE OIG: IDATA MINING IN HEALTH CARE COMPLIANCE AND REGULATION: THE NEXT PHASE IN HEALTH PROGRAM INTEGRITY Jim Sheehan Medicaid Inspector General (518) 473-3782 jgs05@omig.state.ny.us

2. MEDICAID 2006 spending:$303 billion in US (state and federal) 2007: $338 billion (Health Affairs estimate) 2006 spending: $44 billion in New York (Kaiser Family Foundation) 2007:$46 billion New York Medicaid spending growth has slowed recently, remains our national safety net program Majority of adult Medicaid enrollees in New York work; 1/3 of New York City residents (2.8 million) are enrolled in Medicaid (United Hospital Fund) $4 billion prescription drug payer, even after Part D

3. NEW YORK MEDICAID DATA ANALYSIS-THE NATIONAL LEADER Biggest program and payments Biggest program integrity unit-500 employees Biggest data warehouse-$200 billion in claims Leader in transition from prosecution emphasis to program integrity emphasis

4. PROGRAM INTEGRITY WISH LIST We want to measure what we do. We want to build in program integrity on the front end of the program. We want to make data mining tools that work available to providers and their organizations. We want to distinguish among organizations that have effective compliance and quality programs, and those that don�t, in our response to identified improper payments.

5. PROSECUTION FOCUS ON INTENT OF INDIVIDUAL AND ENTITY ACTORS What did they do that was wrong? How did they know it was wrong? How do we prove that they knew? How do we punish them for conduct they knew was wrong, and recover the money they obtained through wrongful behavior?

6. CONSEQUENCES OF PROSECUTION MODEL PAY AND CHASE CRIMINAL FOR LITTLE GUYS, CIVIL FOR BIG GUYS, MONITORS FOR THE BIGGEST GUYS OBSTRUCTION IS THE EASIEST CRIME TO PROVE SKILL-good lawyers make huge difference LUCK Was there a whistleblower? Emails, record destruction, undercover, recorded conversations

7. PROGRAM INTEGRITY-FOCUS ON REDUCING IMPROPER PAYMENTS What improper payments occur? Why do they occur? What systems and controls were in place (at provider, payor, and enrollee) to prevent and detect improper payments? What improvements are required to systems and controls to prevent recurrence? How do we distinguish between organizations with effective systems and controls and those without? If you can�t do it right, we don�t want you in program

8. PROGRAM INTEGRITY MEANS A FOCUS ON EFFECTIVE COMPLIANCE PROGRAMS NY-mandatory �effective� compliance programs for hospitals and snfs Failure to have effective compliance program is basis for exclusion �effective� compliance program requires disclosure to state of overpayments received, when identified �effective� compliance program requires risk assessment, remedial measures

9. Program Integrity and Data Mining Systems Data mining is a developing area � processing speed doubles every two years, software and analytic approaches move at same speed. Existing state data systems, at best, reflect reliable, tested systems and the state-of-the-art at the time of procurement. Existing New York systems procured five years ago, began operating three years ago. Significant opportunities for post-payment recoveries

10. Data Mining Investigative Tools Investigator/auditor experience-data analysis integration What do we want to accomplish? Visualization, data integration, data manipulation Super crunchers - algorithms, regression analysis, standard deviations Who can do this? Who can use this?

11. DATA MINING TECHNIQUES Claims analysis-5 years, $200 billion in claims in data warehouse Patient demographic feed and match-age, sex, marital status, addresses, licensing, ssns Electronic diagnostic and treatment feeds-ICD-9s, DRGs, key words-claims, managed care encounters, authorizations In-depth medical record analysis for given disease conditions using integrated health care organization systems (Bennett and colleagues, Northwestern) Geographic analysis for sales, patients, providers, relationships Modality analysis-which physicians use injectibles? Which physicians are early adopters? Which physicians use lab and physiological diagnostic tests?

12. DATA MINING TECHNIQUES PROVIDER ANALYSIS-CLAIMS SURGES AND OUTLIERS (this provider behaves differently from similar providers) PROVIDER ANALYSIS-�FAIR ISAAC� type RISK SCORING (use proprietary models with multiple tools) REGRESSION ANALYSIS AND ALGORITHM BUILDING-(If it happened this way the last time, we predict it will happen this way again) FUZZY LOGIC-NOT BINARY (yes/no) BUT �SOMEWHAT� (190 lbs. is �somewhat� heavy, �somewhat� normal for adult male, total cholesterol of 220 is �somewhat� high) NEURAL NETWORKS-SYSTEM THAT �LEARNS� THROUGH PATTERN RECOGNITION AND NONLINEAR SYSTEM IDENTIFICATION AND CONTROL. (�BLINK� by Malcolm Gladwell, elected officials and risk tolerance )

13. DATA MININGWhere we Plan to be in FFY 2008 Data mining will be significant contributor to $215 million recovery goal required by CMS contract with New York for 2008 Pilot projects with contractors and DOH Expanded capabilities for out years-systems, contractors and personnel OMIG task force to enhance use of data mining results in audits, investigations and enforcement

14. Data Mining Approaches Historic approach � Clinically supported hospital DRG claim, nursing home MDS review Hospital admission, DRG assignment review Search, sampling, record support Algorithm for follow-up review Dollar limits on recovery per hospital per period, no projection from sample Large range of errors: 2% - 40%

15. Data Mining ApproachesData Matches/Demographics Men having babies Fillings in crowns Deceased enrollees Children under 10 years old having babies Women giving birth every 5 months Women over 50 years old having babies without infertility treatments

16. Data Mining ApproachesData Match on Providers & Networks Doctor P was excluded from Medicaid, but is ordering services Doctor in Amagansett serves only patients from Bronx. Every patient who goes to clinic y gets multiple prescriptions filled in pharmacy Z Dr. A in Rochester writes 10% of the prescriptions filled at chain pharmacy location B. 85% of patients at in-patient detox center J are treated at discharge by D and TC clinic K Managed care plan Q has never paid for a mental health visit for a Medicaid member

17. Data Match Enrollees Multiple enrollments/same plan Multiple enrollments/multiple plans Multiple enrollments/multiple counties PARIS match - multiple enrollments, multiple states Problem enrollments by enroller (not yet done) Multiple enrollees - same SSN

18. Government Partnerships-federal and county Continue to support local districts through the County Fraud, Waste and Abuse Demonstration Project. 13 counties plus New York. 95 audits have been authorized, 28 exit conferences held or pending with $9 million in preliminary findings. Working with CMS to identify additional, best practices approaches to data mining-CMS currently developing Medicaid algorithms for national testing.

19. Data Mining Quality ToolsProviders Not Meeting Minimum Standards Never events Unreported adverse events Unreported adverse outcomes/unanticipated deaths Ranking/rating facilities-audit focus Condition of participation failures (structure) Drug outcomes in populations and in facilities