network detection and analysis

1 / October 2008 / SGS INTERNAL

Network Detection and AnalysisKaren PainterSandra Dorman

Eastern and Pennsylvania Benefit Integrity Support Centers


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


Current Fraud Landscape

•Fraud schemes are evolving and more sophisticated

•Medical management organizations

•Organized crime rings

•Identity theft


Network Detection and AnalysisTraditional Approach


Our Approach - FUSION Model

•Fraud Detection

•Utilization

•Statistical Models

• Integration

•Overpayment

•Network Analysis


Network Detection Examples


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


Beneficiary Sharing - Analysis and Findings


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


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


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


Ambulance – Analysis and Findings


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

0

100,000

200,000

300,000

400,000

Lab Referring Provider

LAB Referring Provider

• 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

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


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


OT and PT Same DOS – Analysis and Findings


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


Identity Theft – Analysis and Findings


Statistical Models


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


Spike Model - Example


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

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


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


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 Patient Example

Trend ModelDollars 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


Static Model - Example


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


Logistic Regression Model - Example


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


Our Approach - FUSION Model


Results

•70+ Fraud Investigations

•15 Referrals to OIG

•Approx $5.1 million identified overpayments

•Approx $4.2 million in pre-payment savings


Questions??


SafeGuard Services, LLC225 Grandview AvenueCamp Hill, PA 17011717 975 [email protected]

network detection and analysis

Documents