Fighting Fraud Collaboratively in Medicare and Medicaid: The Ohio Medi-Medi Project

1. Fighting Fraud Collaboratively in Medicare and Medicaid:  The Ohio Medi-Medi Project Laura Long, OH Medi-Medi Project Manager � AdvanceMed Kevin Jones, Program Integrity Manager Ohio Department of Job & Family Services NAMPI � August 25, 2008

2. 2 Today�s Discussion Introduction to AdvanceMed Medi-Medi Overview Matched Data Sets Homogenizing Data Proactive Studies Sharing Outcomes Discussion of successful collaboration efforts Medi-Medi Advantages Questions/Answers

3. 3 Introducing AdvanceMed AdvanceMed is a Program Safeguard Contractor with the Center of Medicare & Medicaid Services (CMS) AdvanceMed has Medicare Program Integrity contracts for Part B fraud in 21 states and Part A fraud in 17 states. AdvanceMed has contracts for three (3) Medi-Medi Projects: Ohio North Carolina Washington

4. 4 Medi-Medi Overview Purpose: To perform analytic studies on Medicare and Medicaid data to identify potential fraud, waste or abuse (FWA) Requirements from CMS: Create a matched data set for 3 years of claims data Conduct Proactive Studies Identify and investigate providers suspected of FWA Identify program vulnerabilities (both programs) Collect Overpayments Make referrals to law enforcement (OIG), state Medicaid agencies, or Medicaid Fraud Control Unit

5. 5 Creating the Matched Data Set Due to the unique data available from each state, the methods of matching are likely to vary for each Medi-Medi project Some projects may choose to �match� data by the claim Other project create a provider matching algorithm to create a Provider Match ID which is then added to the data sets The keys to provider matching include: Being prepared to handle one-to-many matching results Understand that matching is not an exact science Valid Matches may be missed (e.g. unpopulated fields)

6. 6 Resulting Data Warehouse Example

7. 7 Homogenizing Claims Data Fields are not titled the same Similar titles may have different meanings Not all the same fields are available � Identify common, key fields May be high percentage of unpopulated fields (e.g. referring provider) Layout files are different between the programs Important to use same layouts and labels for data management, aggregating data and scripting purposes AdvanceMed uses DataProbe in data management and data analytics

8. Data Analytics Using Matched Data Examples of Proactive Studies

9. 9 Proactive Studies Using Matched Data Set Specialty outliers across programs Identifies outliers in one or both programs Time Based Studies (Time Bandit) Evaluate total volume and time of services a provider bills to both programs on a single date of service Excessive volume, frequency or modifier use Diabetic strips Incontinence supplies Pharmacy Unskilled home health visits Hypothesis � based studies CPAP device without sleep study in either program DME equipment without E/M visit by ordering physician in prior hx Debridement of 6 or more toe nails when patient had prior lower limb amputation

10. 10 Sharing Outcomes Outcomes may include: Overpayments, actual and extrapolated Program vulnerabilities Policy and/or system edit recommendations Identification of new schemes or patterns Referrals of fraudulent providers to law enforcement (federal and state) Outcomes are shared with state agencies, CMS and law enforcement (federal and/or state) as appropriate

11. 11 Successful Collaboration Time Based Study One provider referred to law enforcement OIG & MFCU working collaboratively on the case Incontinence Supplies Excessive units billed � Data trending upward Found maximum allowable limits were exceeded $223K in overpayments for recipient age < 36 months E1399 & Surrogate Referring UPIN outcomes One provider billed 100% claims with dump code and surrogate referring Found services should not have been covered but were prior authorized. Actions taken to prevent additional payments and improve prior authorization process.

12. 12 Successful Collaboration Ohio Program Integrity Group Meetings include: ODJFS: Policy SURS Auditor of State MFCU AdvanceMed Discuss current investigations & outcomes Collaborate on new trends, schemes & studies

13. 13 The Medi-Medi Advantage Matched data set provides bigger picture of the providers� practice patterns Having suspicious claims and billing patterns in both programs identified for law enforcement provides more leverage for plea agreements, convictions, etc. CMS benefits from the recoupment of both the Medicare money and the federal share of the Medicaid money Working collaboratively sends a message to the providers that the Medicare and Medicaid teams are united against fraud

14. 14 Questions/Answers