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Network Detection and Analysis. Karen Painter Sandra Dorman Eastern and Pennsylvania Benefit Integrity Support Centers. Introduction. Traditional Data Analysis Approaches. Individual providers High dollar billers Spike reports Top procedure codes Individual specialties.
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Network Detection and Analysis Karen Painter Sandra Dorman Eastern and Pennsylvania Benefit Integrity Support Centers
Traditional Data Analysis Approaches • Individual providers • High dollar billers • Spike reports • Top procedure codes • Individual specialties
Current Fraud Landscape • Fraud schemes are evolving and more sophisticated • Medical management organizations • Organized crime rings • Identity theft
Network Detection and Analysis Traditional Approach
Our Approach - FUSION Model • Fraud Detection • Utilization • Statistical Models • Integration • Overpayment • Network Analysis
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
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
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
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
Laboratory – Analysis and Findings • Trend of laboratory and referring provider relationship
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
Physical Therapy – Analysis and Findings • Trend of Diagnosis Code for Group billing PT & OT
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
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
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
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
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
Outlier Model – Patients per Day Example Mean = 9.88 Median = 6.79 Standard Deviation = 9.58 Threshold for Outliers using Quartile Method = 28.49
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
Trend Model – Dollars per Patient Example Trend Model Dollars per Bene for a Specialty 18 Provider
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
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
Results • 70+ Fraud Investigations • 15 Referrals to OIG • Approx $5.1 million identified overpayments • Approx $4.2 million in pre-payment savings
SafeGuard Services, LLC 225 Grandview Avenue Camp Hill, PA 17011 717 975 4434 Karen.L.Painter@eds.com