Network Detection and Analysis

1. Network Detection and Analysis Karen Painter Sandra Dorman Eastern and Pennsylvania Benefit Integrity Support Centers

2. Introduction

3. Traditional Data Analysis Approaches • Individual providers • High dollar billers • Spike reports • Top procedure codes • Individual specialties

4. Current Fraud Landscape • Fraud schemes are evolving and more sophisticated • Medical management organizations • Organized crime rings • Identity theft

5. Network Detection and Analysis Traditional Approach

6. Our Approach - FUSION Model • Fraud Detection • Utilization • Statistical Models • Integration • Overpayment • Network Analysis

7. Network Detection Examples

8. Utilization Detection - Beneficiary Sharing • Started with a known provider group suspected of sharing beneficiaries • Gathered all data on the beneficiaries • Identified 3,947 providers and 1,487 beneficiaries • Identified 274 providers and 541 beneficiaries through a dense cluster analysis

9. Beneficiary Sharing - Analysis and Findings

10. Utilization Detection - Husband/Wife • Found 1,800 instances of husband/wife beneficiaries • Receiving the same procedure • With the same diagnosis • On the same date of service • With the same provider • 48 providers rendered services to these pairs • One pair had a total of 22 different diagnosis codes • Total Paid $425,256

11. Husband/Wife – Analysis and Findings

12. Utilization Detection - Ambulance • Identified Beneficiaries with transports of 5 or more different ambulance companies per year • Identified transports to nowhere • Currently under law enforcement investigation

13. Ambulance – Analysis and Findings

14. Utilization Detection - Laboratory • Laboratories identified through ‘traditional’ spike models • Analysis of referring providers uncovered suspect relationships • Comparison of laboratory claims/diagnosis and treatment by the referring provider uncovered inconsistencies

15. Laboratory – Analysis and Findings • Trend of laboratory and referring provider relationship

16. Utilization Detection - Physical Therapy • Started with all beneficiary and provider combinations for PT (97110) • Narrowed dataset to instances where beneficiaries saw 5 or more providers for 97110 within 1 year • Identified a set of 522 providers • Identified 318 beneficiaries

17. Physical Therapy – Analysis and Findings • Trend of Diagnosis Code for Group billing PT & OT

18. Utilization Detection - OT and PT Same DOS • Beneficiaries who received occupational therapy and physical therapy on the same day • Analysis on 3 month period • A total of 308 providers were identified • A total of 753 beneficiaries

19. OT and PT Same DOS – Analysis and Findings

20. Utilization Detection – Identity Theft • Approach was to look for beneficiaries that had a sudden increase in the number of carriers • Looked for a spike in payment for our beneficiaries out of state • Looked for out of state beneficiaries in our jurisdiction

21. Identity Theft – Analysis and Findings

22. Statistical Models

23. Spike Model • Goal is to identify providers with a large increase (spike) in dollars paid • Compare one recent month with a calculated baseline average (Previous 6 or 12 months) • Identify providers with a 100% increase and a minimum of $50,000 paid in current month

24. Spike Model - Example

25. Outlier Model • Goal is to identify providers that are not like their peer group (i.e. same specialty) • Two complex variables are considered: • Dollars per patient • Patients per day

26. Outlier Model – Dollars per Patient Example Mean = 148.29 Median = 110.90 Standard Deviation = 106.09 Threshold for Outliers using Quartile Method = 403.28 Threshold for Outliers using a Z-Score of 2 = 360.47 Threshold for Outliers using a Z-Score of 3 = 466.56

27. Outlier Model – Patients per Day Example Mean = 9.88 Median = 6.79 Standard Deviation = 9.58 Threshold for Outliers using Quartile Method = 28.49

28. Trend Model • Goal is to find providers that may not have ‘spiked’ but have had a statistically significant increase over a six month period • Trend is evaluated on two complex variables • Dollars per patient • Patients per day

29. Trend Model – Dollars per Patient Example

30. Trend Model – Dollars per Patient Example Trend Model Dollars per Bene for a Specialty 18 Provider

31. Static Model • Goal is to identify providers that consistently bill the same set of procedure codes • For example: office visit, blood test, urine test, for each beneficiary • Potential to expand to diagnosis codes or other parameters

32. Static Model - Example

33. Logistic Regression Model • Goal is to identify providers with a similar profile of known fraudulent/abusive providers • Create a model based on historical data and then apply this model to current data • Providers with patterns similar to providers already found to be fraudulent are flagged for review

34. Logistic Regression Model - Example

35. Integration of Statistical Models

36. Our Approach - FUSION Model

37. Results • 70+ Fraud Investigations • 15 Referrals to OIG • Approx $5.1 million identified overpayments • Approx $4.2 million in pre-payment savings

38. Questions??

39. SafeGuard Services, LLC 225 Grandview Avenue Camp Hill, PA 17011 717 975 4434 Karen.L.Painter@eds.com