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Slides
Innovative Fraud Detection at CMS
Mike Doane, Technical Director Federal Healthcare
April 10, 2013
Agenda
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Fraud Characteristics
CMS Requirements
MarkLogic POC Functionality
Use Cases
Future Capabilities
Q&A
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Healthcare Fraud is Huge
FY 2012
90
80
70
60
50
40
30
20
10
0
$B
80
4.2
Total Fraud (FBI Est.) Amount Recovered
New approaches to detection are needed
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1.6
Spending on
Recovery
What does fraud look like?
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False storefronts
Beneficiary shopping (compromised benes)
Networks of fraudsters
Ambulance trips – no check-in
Home health
Double-billing
DME proliferation
ID theft
Prescriptions (forged, resold, etc.)
Upcoding
Kickbacks
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Today’s Approach
 Sharding of databases by states and territories
 Manual correlation across states
 Multiple versions of algorithms
 Looking at only the claims
 Looking at these claims well after they’ve been
submitted
 Not incorporating other available sources
 Not sharing results
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CMS Asked For
 Scalability to evaluate the entire country’s data as a single,
unified data set
 The Agility and Flexibility needed to keep up with changing
data sources and evolving data analysis strategies
 Better Performance - a shorter Time To Market for its data
 Facilitation of Enterprise Data Sharing within CMS and with
CMS partners
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Scalability
Over 600M documents, 4+TB in the POC
Deliver a 300TB+ cluster and eliminate
sharding of data by geography
Fifty states plus six territories
Unknown additional sources
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Agility and Flexibility
Load data as-is
Handle combination of full text (unstructured) and structured data
Support in-line data descriptions or enrichment
Allow composable queries across all data
 Full-text search, geospatial constraints, and traditional and fielded
constraints all in the same query
Handle Unpredictability
 New data of any type readily incorporated without schema
changes – indictment docs, Dunn & Bradstreet, Facebook, Property
records
Support flexible Provider and Beneficiary profiles
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Flexible Profiles
Provider
NPI
Associations
Beneficiary
Metadata
Statistics
Assoc
Hosp
RxCount
Assoc
-Prov
Providers
Visit
Bill-Amt
Addresses
InclusionAddress reason
ID
Address
Address
Benedistance
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Annual
Billings
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Early- Distance TopschedII
refill
-Rx
Flexible Profiles
Provider
NPI
BoardMember
Assoc
Hosp
RxCount
Associations
Beneficiary
Providers
Metadata
Statistics
Visit
Assoc
-Prov
Exclusion
Addresses
InclusionAddress reason
ID
Bill-Amt
Address
Address
Benedistance
MRIcount
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Annual
Billings
DEA
Indictment
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Early- Distance TopschedII
refill
-Rx
PersonalProperty
Auto House
$-value
Ambulancecount
$-value
Performance – Time to Market
Operate on valid and contemporary data
Handle multiple disparate data sources with varying
schemas
 Enrich in place by reference and related data sources
Architect for performance and flexibility
 Distribute loading across servers in the cluster
 No model changes for new incoming formats
 Database and Data Warehouse are the same – no data
staging between products
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Enterprise Data Sharing
Support direct data access from external systems
 Connectivity via Java API, REST API, .NET,
ODBC/SQL
Facilitate sharing data back to the states or other parties
 Expose data via SOAP or REST web services
Provide efficient transformation of data to meet third
party required exchange formats
Slide 17
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MarkLogic Delivered
SAS, Excel
Fraud Review
Application
Alerting
APS
Load data “as is”
MSIS Claims
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MDEM Claims
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NPI
Reference
Data
Indictments
SAS AFRs
POC Functionality
 Provider and Beneficiary Profiles
 Search
 Detailed Views
 Easy Risk Scoring
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Granular State Policy Document Search
Contextual Enrichment of Alerts
Incorporation of External Alerts
Co-occurrence Analysis
Link Analysis
Geospatial Analysis
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Link/Network Analysis
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Geospatial Enrichment & Analysis
<provider-address lat="31.25" lon="99.25">
<addr-line1> 1214 Main </addr-line1>
<city> Austin</city>
<state> TX </state> <zip>98143</zip>
</provider-address>
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State Policy Documents
Slide 22
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Contextual Enrichment of Alerts
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POC Findings
 Aberrant behavior based on NPI registration
 Compromised beneficiaries
 Value of external data sources to support link
analysis
 Aberrant behaviors based on high billings
 Aberrant behaviors based on high procedure codes
 Aberrant behaviors based on geospatial data
 Providers that should have been excluded based on
sanctions, exclusion lists
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Use Case 1: Provider Profiles
Clifford Ubani
NPI registration (data enrichment)
Address coordinates
Dunn & Brad Street incorporation (external data sources)
Ability to link additional participants (unknown players)
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Profile of Clifford Ubani – Multiple NPI
Clifford Ubani
Family Health Services
8313 Soutwest Fry,
Suite 109
Houston, TX
Home Health
Directcare Link
8313 Southwest Fry,
Suite 113
Houston, TX
DME
Family Health
Services (Family
DME)
8313 Southwest Fry,
Suite 113
Houston, TX
DME
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St. Michaels EMS
9896 Bissonet St.
Suite 340
Houston, TX
Ambulance
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Special Care
8313 Southwest
Fry,
Suite 215
Houston, TX
DME
Ubani – Common Address
Family Health Services
8313 Southwest Fry, Suite 109
Houston, TX
Home Health
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CKCC
8313 Southwest Fry, Suite 109
Houston, TX
Home Health
Ubani – Dunn & Bradstreet
Dunn & Bradstreet links Clifford Ubani to:
 Family Healthcare
 Person of Interest – Princewill Njoku (CCK DME) 8313 Southwest Fry,
suite 107
 Future EMS
 Persons of Interest Monica Stahl, Franklin Godfrey, Wissam Moustafa
Zanhar
 St. Michaels EMS
 Person of Interest Stephen Onyechi
 Ballet Healthcare
 Person of Interest Ededem Edem (Edison Medical Corporation)
 Special Care
 Person of Interest Caroline Njoku
 UBA Holdings
 Person of Interest Ezinne Ubani
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Shared Beneficiaries
Family Health Services and CKCC
Home Health
Family Health Services DME and
CKCC Home Health
Robert Hill
Robert Hill
Deborah Fenroy
Deborah Fenroy
Mitzie Wilson
Elbert McNeil
Elbert McNeil
Iva Parkhill
Iva Parkhill
Ester Dickey
Jeff Ferguson
Clarence Green
Ester B. Dickey
Esther Hunter
Clarence Green
Lee Parkhill
Esther Hunter
Ruth Johnson
Lee Parkhill
Gerald Daniels
Carolyn Schexnayder
Ruby Haywood
Ruth Johnson
Lulla Brooks
Gerald Daniels
Norma Lezine
Ruby Haywood
Morris Paul
Lulla Brooks
Zelda Paul
Norma Lezine
Dorothy Anderson
Morris Paul
Zelda
Paul
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Slide 29
Dorothy Anderson
Ubani - Medicare Indictment
Medicare Indictment Document - June 20, 2010
Clifford Ubani, Ezinne Ubani, and Princewill Njoku (registered
nurse) owners of Family Health Services
Mary Ellen (vocational nurse) (recruiter)
Rolondae Mitchel Straughter (recruiter)
Caroline Njoku (recruiter)
Michelle Turner (recruiter)
Cynthia Garza Williams (recruiter)
Slide 30
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Use Case 2: Link Analysis
Devon Michael Spicer Multiple NPI
Brainex Consulting
1962702597
7447 Harwin Drive,
Suite 217
Houston, TX 77036
Mindex Consulting
17506811466
POC Value – Linking Devon Michael Spicer multiple NPI
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Indictment Link – Devon Michael Spicer
Mobility Store
Devon Michael Spicer
Indictment
Adding external data sources – Indictments – elevates Devon Michael Spicer
as Person of Interest and need to further examine his associations and ties
to other Medicaid Providers
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Persons of Interest – Verona Spicer
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Linking Known Entities to Examine Aberrant
Behaviors and Relationships
Verona Spicer
Invision Pros
Devon Michael Spicer
Elite Care Medical Services
7447 Harwin Drive,
Suite 109
Houston, TX 77036
1740298868
Brainex Consulting
1962702597
Mindex Consulting
17506811466
POC Value – Linking Claims and Non Claims Data (Dunn & Bradstreet) to find
Suspect relationships and billing behaviors
Slide 34
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Use Case 3: Using Density Map to find “unknown”
patterns and risk behaviors
8303 Southwest Freeway, Houston, Texas
7 Home Health Agencies
– Do they share any beneficiaries
– Any beneficiaries billed with aliases
– High billable procedure codes
 Coastal Medical Services
– 1144305004 509249318 DANIELLE HAMILTON Coastal Medical
Services 1144305004 510322382 DANNY HAMILTON Coastal Medical
Services 1144305004 513606514 DE A HAMILTON Coastal Medical
Services
 Houston Optimum Care Associates
– 1144305004 512127894 ROBERTHA COX Coastal Medical Services
1740300540 512127894 ROBERTHA COX Houston Optimum Care
Associates
– Suspicious Doctor – Doctor Emanuel Nwora – Vestibular Diagnostic
Codes – one patient billed 800 times
Slide 35
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What’s Next
 More States, more claims, more fields provided
 Enrichment of provider’s notes
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A better format for data delivery
Additional Data Sources
Access DMV and home ownership records
Hadoop integration
Enhance algorithms with profiles – run as UDFs
Leverage Semantic Tech
 Store Provider to Provider and Beneficiary to Provider as triples
 Provider and Beneficiary profiles as hierarchical docs in XML
 Traverse triples graph for relationships and focus using queries on
profiles
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And There’s Too Much Waste
Estimated Waste in US Healthcare Spending
Unnecessary Care
Fraud & Abuse
Administrative Inefficiency
Medical Mistakes
Preventable Conditions
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Any Questions?
Slide 38
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For More Information
Mike Doane, Technical Director Federal Healthcare
[email protected]
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