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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. - PowerPoint PPT Presentation
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1 / October 2008 / SGS INTERNAL
Network Detection and AnalysisKaren PainterSandra Dorman
Eastern and Pennsylvania Benefit Integrity Support Centers
2 / October 2008 / SGS INTERNAL
Introduction
3 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Traditional Data Analysis Approaches
•Individual providers
•High dollar billers
•Spike reports
•Top procedure codes
•Individual specialties
4 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Current Fraud Landscape
•Fraud schemes are evolving and more sophisticated
•Medical management organizations
•Organized crime rings
•Identity theft
5 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Network Detection and AnalysisTraditional Approach
6 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Our Approach - FUSION Model
•Fraud Detection
•Utilization
•Statistical Models
• Integration
•Overpayment
•Network Analysis
7 / October 2008 / SGS INTERNAL
Network Detection Examples
8 / OCTOBER 2008 / SGS INTERNAL Network Detection and 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
9 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Beneficiary Sharing - Analysis and Findings
10 / OCTOBER 2008 / SGS INTERNAL Network Detection and 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
11 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Husband/Wife – Analysis and Findings
HIC Bene First
NameBene Last
Name
Bene Gender Desc
Claim First
Date of Service
Rendering Prov Name
Referring Prov Name
Line Dx
Code CPT
Bene Current Street 1
Adr
Bene Current Street 2
AdrPaid Amt
HIC1 Jane Doe FEMALE 01/05/07 Physician A Physician A 7390 98927123 Main
StAPT 3H $45.54
HIC2 John Doe MALE 01/05/07 Physician A Physician A 7390 98927123 Main
StAPT 3H $45.54
HIC3 Minnie Smith FEMALE 01/02/07 Physician B Physician C 7393 98942987 Smith
StAPT 4G $38.85
HIC4 Mickey Smith MALE 01/02/07 Physician B Physician C 7393 98942987 Smith
StAPT 4G $38.85
HIC5 Fannie Jones FEMALE 11/28/07 Physician D Physician D 4280 99213456 South
StAPT C-4 $55.69
HIC6 Fred Jones MALE 11/28/07 Physician D Physician D 4280 99213456 South
StAPT C-4 $55.69
12 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Ambulance – Analysis and Findings
14 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Laboratory – Analysis and Findings
0
100,000
200,000
300,000
400,000
Lab Referring Provider
LAB Referring Provider
• Trend of laboratory and referring provider relationship
16 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Physical Therapy – Analysis and Findings
• Trend of Diagnosis Code for Group billing PT & OT
0
20
40
60
80
Jan-0
6
Mar-06
May-06
Jul-0
6
Sep-06
Nov-06
Jan-0
7
Mar-07
May-07
Jul-0
7
Sep-07
Nov-07
Jan-0
8
Mar-08
Diagnosis Trend PT/OT Group
7245
7242
7231
7197
71516
71511
18 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
OT and PT Same DOS – Analysis and Findings
20 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Identity Theft – Analysis and Findings
22 / October 2008 / SGS INTERNAL
Statistical Models
23 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Spike Model - Example
25 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Outlier Model – Dollars per Patient Example
0 60 120 180 240 300 360 420 480 540
0
5
10
15
20
25
30
Mean = 148.29Median = 110.90Standard Deviation = 106.09Threshold for Outliers using Quartile Method = 403.28Threshold for Outliers using a Z-Score of 2 = 360.47Threshold for Outliers using a Z-Score of 3 = 466.56
27 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Outlier Model – Patients per Day Example
3 9 15 21 27 33 39 45 51 57 63 69 75 81 87 93 99 105
0
10
Mean = 9.88Median = 6.79Standard Deviation = 9.58Threshold for Outliers using Quartile Method = 28.49
28 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Trend Model – Dollars per Patient Example
30 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Trend Model – Dollars per Patient Example
Trend ModelDollars per Bene for a Specialty 18 Provider
31 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Static Model - Example
33 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
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 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Logistic Regression Model - Example
35 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Integration of Statistical Models
Provider SPCSpike Aug07
Trend - $$/Bene
Trend - Benes/Day
Outlier - $$ /Bene
Outlier- Benes/
Day
Static Utilization
of CPT Codes Complaints SUM $$ Pd Comments
A 11 1 1 1 3 $ 626,121 Active Case
B 08 1 1 1 3 $ 173,631
C 18 1 1 2 $ 142,829
D 30 1 1 2 $ 130,000
E 65 1 1 2 $ 150,000
F 11 1 1 2 $ 120,355 Active Case
G 83 1 1 2 $ 109,722
36 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Our Approach - FUSION Model
37 / OCTOBER 2008 / SGS INTERNAL Network Detection and Analysis
Results
•70+ Fraud Investigations
•15 Referrals to OIG
•Approx $5.1 million identified overpayments
•Approx $4.2 million in pre-payment savings
38 / October 2008 / SGS INTERNAL
Questions??
39 / October 2008 / SGS INTERNAL
SafeGuard Services, LLC225 Grandview AvenueCamp Hill, PA 17011717 975 [email protected]