Download Data Mining - Health Care Compliance Association

Health Care Compliance Institute
Data Mining Workshop (P17)
Marita Janiga, Director, National Special Investigations Unit
Jay Loden, Assistant Director, Data Analytics
April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Agenda
Introduction to Kaiser Permanente
Information Analytics and Compliance Technology (iACT)
Data Mining Studies
National Special Investigations Unit (NSIU)
Prescription Drug Abuse
Collaboration between iACT and NSIU on actual cases
2 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
1
Founded in 1945, Kaiser Permanente is one of the nation’s largest not-forprofit health plans, serving more than 8.9 million members.
Northwest
 Total Members: 476,345
Northern California
Ohio
 Total Members: 3,263,619
 Total Members: 122,342
Mid-Atlantic States
 Total Members: 488,171
Colorado
 Total Members: 526,258
Southern California
 Total Members: 3,341,646
Georgia
 Total Members: 222,074
Hawaii
 Total Members: 229,186
*Membership as Dec 31, 2010
3 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Kaiser Permanente by the Numbers
Total membership 8.7 Million
Hospitals
Medical offices
Physicians
36
533
15,853
Approximate, representing all specialties
Employees
167,178
Approximate, representing technical, administrative, and clerical employees and
caregivers (includes 45,270 nurses)
Doctor office visits (annually)
Prescriptions filled (annually)
Number of outpatient pharmacies
36.6 Million
132.2 Million
383
*Data as Dec 31, 2010
4 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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Kaiser Permanente Pharmacies Nationwide
To serve 8.6 million members……
Outpatient Pharmacies
400
Central Fill & Mailer Pharmacies
8
Inpatient Pharmacies
30
Chemo, Home Health, Specialty Pharmacies
30
Pharmacy Call Centers
Multiple
Pharmacy Warehouses
Total Pharmacy Personnel
5 April 29, 2012
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8
9,000
© 2012 Kaiser Foundation Health Plan, Inc.
National Compliance Office
Senior Vice President and Chief
Compliance Officer
National Compliance
Departments
Vice President Ethics,
Compliance and Fraud
Control
Human Resources
Vice President Medicare
& Compliance Program
Vice President
Compliance Strategy &
Operations
Colorado
iACT
Compliance
Programs
Business Services
Compliance Hotline
& NSIU
Medicare & Federal
Programs
Health Plan, HCR,
Compliance &
Strategy Operations
Government Audit &
Reimbursement
Privacy, Security &
Compliance
Program
Strategic
Communications &
External Relations
Learning &
Awareness
Audit
Georgia
Facilities Services
Pharmacy
MSSA
Regional Compliance
Offices
EH&S
Finance
Mid-Atlantic
Northern California
Northwest
IT Compliance
Community Benefit
Hawaii
Care Delivery
Ohio
Southern California
KFRI
6 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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Information Analytics Compliance and
Technology (iACT)
7 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Who is NCO iACT?
Information Analytics & Compliance Technology
iACT’s Mission: Provide credible and accurate analytics; Improve organizational
performance and regulatory compliance; Create stronger safeguards for our
assets and members
Partners: Include Pharmacy, Revenue Cycle, Financial Service Operations
(A/P), Claims, Compliance Community & NSIU
Tools & Data Sources:
– External Data Warehouse of over 60 Terabytes of non encounter data in 8 categories
including claims, Claims, Membership & Pharmacy across all regions.
– Internal Data Warehouses (Electronic Medical Records, over 300 data warehouses)
Pro-Active (Targeted) Data Mining: Fraud control, compliance monitoring &
cost containment work plan studies and ad hoc reporting
Re-Active Data Mining: National and regional fraud investigator ad hoc
reporting. Office of Inspector General, Federal Bureau of Investigations, and
law enforcement alert data mining.
8 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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iACT Sources and Tools
 iACT’s access and versatility
– Internal and external data “warehouses” (inc. vendor facilitated – HOPS)
 Revenue Cycle & Care Delivery
– Kaiser Permanente HealthConnect, ePremis
 Health Plan
– Claims, Accounts Payable, Membership, Payroll
 Pharmacy
– PIMS, PAS, PDE
 Special Data
– Social Security Death Master, CMS Enrollment & Disenrollment, and others
9 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Data Mining – Current And Future
Current State
Future State
Transaction
Analysis
Anomaly
Analysis
Predictive
Analysis
• rules based
• – if / then
• detect abnormal
patterns, outliers,
comparative –
aggregate or peers
• modeling against
known and
unknown fraud
cases
10 April 29, 2012
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Network Link/
Neural
Analysis
• discovery through
associative links,
usually hidden
layers below
common data
© 2012 Kaiser Foundation Health Plan, Inc.
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Data Analytics
Regulatory requirements state that Kaiser Permanente have a
“robust” fraud, waste, & abuse program including data mining
Pro-Active (Targeted)
 Fraud Control
– Drug Diversion & Drug Seeking Behavior
 Compliance Monitoring
– CMS High-Risk Counties, Part D
 Cost Containment
– Medicare Coordination of Benefits (COB)
Re-Active
– Regulatory requirements that Kaiser Permanente respond “promptly” to
identified fraud, waste, & abuse concerns
 National & Regional Fraud Investigations
– Vendors, Members, External Providers, Employees
 External Entities
– Federal & State Regulatory / Law Enforcement Agencies and Contractors
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© 2012 Kaiser Foundation Health Plan, Inc.
Reactive Data Mining
National
Compliance
Teams
Requests for data and analysis
come from all areas
Law
Enforcement
Alerts
Monitoring
External
Audits
Hotline
Internal Audit
National
Special
Investigations
Unit
Board of
Directors
iACT
Data
Analytics
Regional
Compliance
Offices
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Pharmacy
Audit &
Compliance
© 2012 Kaiser Foundation Health Plan, Inc.
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Proactive and Reactive Data Mining
Approximately 75% of data
hosted for FW&A also supports
the functional areas overlapping
with “Program Integrity”
Data supports a Proactive and
Reactive comprehensive FWA
data mining program
Our Data Mining program runs
over 1,200,000 queries per year
Fraud
Detection
Quality of Care
Program Integrity
Audit
Plan Design
Correct Payment
Compliance
Coordination
of Benefits
13 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
FWA Program Total Recoveries and Avoidances
14 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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Kaiser Data Footprint:
Internal & External Repository

Counts and Amounts:
Over 60 Terabytes of non encounter data
8 Categories with 69 Active Data Sources
5,605 tables (12% increase)
143,243 columns (8.5% increase)
18.8 billion records (10% increase)
•
•
•
•
•

Data is refreshed:
Daily
Bi-Weekly
Monthly
Quarterly
On-Demand
•
•
•
•
•

4 sources
14 sources
44 sources
6 sources
3 sources
Encounter data:
> 1 petabyte (1000 terabytes)
All encompassing: Physician notes, Lab, Radiology, Pharmacy orders, etc
•
•
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© 2012 Kaiser Foundation Health Plan, Inc.
Pharmacy Data Warehouse Volume
We partner with Kaiser Permanente’s Pharmacy
Analytical Services (PAS) which maintains a
warehouse of 1.2 petabytes of data
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© 2012 Kaiser Foundation Health Plan, Inc.
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Pharmacy Data Warehouse Volume
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© 2012 Kaiser Foundation Health Plan, Inc.
Pharmacy Data Warehouse Volume
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© 2012 Kaiser Foundation Health Plan, Inc.
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Data Analytics: Data & Process Flow
– Current State
Improving Process Management - Leveraging Technology
Operational Data
Sources
Predefined Queries
Pulling data
Claims
BI Tool
Maintained
externally
Ad Hoc reports and
studies
Work plan
Alerts response
Other
29 work plan studies
Membership
•13 produce by
vendors
Ad-Hoc Proactive /
Reactive
•16 produced by iACT
Requests for
Algorithm creation
False positives
review and
elimination by iACT
Internal code
library
3rd Party
Internal Kaiser
Sequel
programming
Pharmacy
Provider
19 April 29, 2012
Raw data,
normalization and
deployment
|
Ideation and Creation
based on Work plan, TW,
other
ROI , Investigation Report
out
© 2012 Kaiser Foundation Health Plan, Inc.
The Fraud Continuum
Forged
Altering days
Voided
prescription
supply
transactions
Altered Rx / false
Quantity mistakes
Data entry / logs
I.D.
Drug diversion
error
abuse
fraud
NOTE: iACT does not determine fraud
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© 2012 Kaiser Foundation Health Plan, Inc.
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Fraud Detection Compliance: Why We Need It
Regulatory Compliance
We need to continue to build a proactive program wide Fraud detection and prevention to be in
compliance with fraud prevention requirements. For example:
• U.S. Sentencing Commission Guidelines, Chapter 8B2.1 – Effective
Compliance & Ethics Program
• Medicare 42 CFR 422.501(b)(vi) Subpart K – Contracts with Medicare Advantage Organizations
• FEHB Program Industry Standards for Fraud and Abuse Programs (Section 1.9(a): Federal
Employees Health Benefits Program
• Medicare Modernization Act (MMA) Part D Compliance Plan Requirements, 42CFR
423.504(b)(4)(vi)/ CMS Part D Manual Chapter 9
• Patient Protection and Affordable Care Act (aka Obama Care)
Noncompliance with Federal guideline requirements for a Fraud detection and prevention program
can result in the loss of Federal contracts and/or reimbursements including Medicare, fines, and
penalties.
21 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Fraud Detection Compliance: Why We Need It, cont.
 Good Business
 Identifies fraud over time, scope, geography. Many Frauds continue over a
long period of time or move to new locations.
 Being proactive could prevent adverse publicity that could affect Kaiser
Permanente’s image and membership growth.
 Visible program and zero tolerance can deter fraud.
 Supports Kaiser Permanente’s commitment to Sarbanes Oxley (SOX)
financial controls management.
 Improved employee satisfaction through the demonstration of our
commitment to a fair and well managed business.
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© 2012 Kaiser Foundation Health Plan, Inc.
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Pharmacy Data Mining ~ Proactive Studies
Current Studies
Areas Under Review
•
•
•
•
•
• Pharmacy Cash Control – Copays and OTC Inventory Control
• Medicare B DMERC – Exposure
Assessment for Part B Drug
• Part D vs. Part B Drugs
Prescribing Patterns, Script Mills
Drug Seeking Behavior
Pharmacy In / Out Study
Prescriptions After Death
Part D: PBM FWA Study –
Excluded Drugs (Part D
Compliance)
23 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Pharmacy Data Mining
High Level Data Review Process: Interaction with iACT and PAS
Review and
eliminate false
positives
through
enhanced
coding
Assessment of
variances –
Review and
send to
appropriate
designee
Output sent
to Pharmacy
Audit
Compliance
for review
Algorithms
run at HOPs
monthly
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Investigation /
Corrective
Action Process
Output is
distributed to
either NSIU or
Regional
Pharmacy
Management
© 2012 Kaiser Foundation Health Plan, Inc.
12
Drugs Analyzed ~ Sample:
We data mine all National Drug
Code’s (NDC) for these drugs.
(generic and brand)
Total drugs reviewed >700
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© 2012 Kaiser Foundation Health Plan, Inc.
Current Data Mining ~ Expanded drug list
We data mine all
NDC’s for these
drugs.
(generic and
brand)
Total drugs
reviewed = 468
26 April 29, 2012
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Computer power!
700 / 3 = 233
Total studies produced
monthly 233x400 =
93,200
© 2012 Kaiser Foundation Health Plan, Inc.
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Pharmacy Data & Investigations Continuum
iACT Data Analytics
Discovery
Pharmacy Audit
& Compliance
Validation
National Special
Investigations Unit
Investigation
•Hotline
•Other
•Data Mining
•Proactive & Reactive
27 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Total $$’s Billed
Physician Billings vs. Visits: Outlier Analysis
# of Visits
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© 2012 Kaiser Foundation Health Plan, Inc.
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Outlier Analysis
Total $$’s Billed
Bad
guy!
# of Visits
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© 2012 Kaiser Foundation Health Plan, Inc.
Raw Data ~ Summary Output
30
April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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Raw Data - Graphed
Variance Report – Viagra 100 mg Tab
Diversion
Begins
31 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
One Touch Ultra Test Strips, cont.
Baseline data from a similar pharmacy
Pharmacy Comparison
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© 2012 Kaiser Foundation Health Plan, Inc.
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Drug Diversion Case:
One Touch Ultra Test Strips
Diabetic
Strips
Diabetic Test
Test Strips
10/12/08 Transfers away from Pharmacy
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© 2012 Kaiser Foundation Health Plan, Inc.
One Touch Ultra
Diabetic Test Strips
10/13/08 Transfers into New Pharmacy
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© 2012 Kaiser Foundation Health Plan, Inc.
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One Touch Ultra Test Strips, cont
Law Enforcement provided NSIU with Pay Pal Transaction Data
35 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Prescribing Patterns / Script Mills
 Pharmacy 70031 Prescribing Patterns / Script Mills
– This report identifies/reviews prescribing patterns of Kaiser Permanente
Physician’s that are prescribing prescriptions for the top 100 drugs in the
specified time frame.
 Considerations
– Data Source: PAS Prescriptions
– Date Range: June 2006 to Dec 2006
– Logic
 Pull in all prescriptions for the specified time range.
 Extract all prescriptions for these 100 drugs and derive the total count of
prescriptions by Prescriber
 Rank all the physicians based on prescription count and proceed with the top 100
– Level Description
 Level 1 is a Summary report of Top 100 Prescribers prescribing drugs for the top
100 drugs
 Level 2 is a Detail Report of 100 members of each physician identified in the
previous level
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© 2012 Kaiser Foundation Health Plan, Inc.
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Prescribing Patterns / Script Mills
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© 2012 Kaiser Foundation Health Plan, Inc.
Prescribing Patterns / Script Mills
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© 2012 Kaiser Foundation Health Plan, Inc.
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Prescribing Patterns / Script Mills
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© 2012 Kaiser Foundation Health Plan, Inc.
Drug Seeking Behavior ~ Rapid Refills

Pharmacy 70076 Drug Seeking Behavior 1.B
 This report identifies members with prescriptions in the pharmacy data who have the same
drug filled within two weeks or less of the original prescription for the same drug

Considerations
 Only the top 2000 members ranked based on the difference in refill days are displayed in this
report




40 April 29, 2012
Data Source: Hawaii Prescriptions
Date Range: June 2006 to April 2007
Logic
 Pull in all prescriptions for the specified time range.
 Using Patient + Prescription + Drug as a key - join prescriptions to each other and filter out the
records where the difference between the Fill Dates is < 15
 Identify the most popular drug being filled by each of the top 2000 members.
Level Description
 Level 1 is a Summary report of Top 2000 identified prescriptions
 Level 2 is a Detail report enlisting the prescription details for each of these top members
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© 2012 Kaiser Foundation Health Plan, Inc.
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Drug Seeking Behavior Scoring Grid
Purpose
Variable
1. Fake identity / False Rx
Non-member or member inactive
Max Points
10
2. False Rx
% of new Rx with no doctor visit
10
50% = 8, 25% = 5, 10 - 24% = 3
3. Multiple sources
# Kaiser Permanente pharmacies - 5 = 10pts, 3 = 5pts
10
4. Multiple sources
# prescribers
10
5 = 10pts, 3 = 7pts
5. Drug User
# of other fraud prone drugs
10
5 =10pts, 4 = 8pts, 3 = 6pts
6. Existing Medical Condition
# doctor visit not involving this drug
10
0 gets 10pts, 6 + gets 0 pts
7. Desperation
Distance traveled vs. non-fraud prone scripts
10
20miles = 10pts, 15 = 7pts
8. Heavy usage
12-month total quantity w/o cancer diagnosis and w/o PM
specialist… Metric is Drug / time period dependent
9. Escalating usage
Slope of dose per day, upward = 10, flat = 5
10
10. Prolonged usage
# months with purchase w/o cancer diagnosis and w/o Pain Mgmt. specialist
10 months or more = 10pts, 6 – 9 = 5pts
10
Drug Seeking Behavior Scoring Grid
10
(cont)
Purpose
Variable
11. Internal administered drugs
Identify if patients received back office injections of C-II High Abuse Potential
controlled substances ONLY. Assign score of >= 10 = 10 pts, 5 = 5 pt., 4 = 4
pts, 3 = 3 pts, 2 = 2 pts, 1 = 1 pt.
Max Points
10
12. Substance abuse
encounters
Identify within the population Drug Abuse/Overdose encounters and related
prescriptions based on ICD9 with the following Diagnosis Codes: 292.89,
305.9, 305.90, 305.91, 305.93, 977.9, 977.90. Assign SCORE of 10 pts if any
of these codes exist.
10
13. CII’s between office visits
Calculate the number of controlled prescriptions filled since last MD office visit
(find last office visit and count how many controlled prescriptions were
dispensed from that visit).
Report count
14. Days between office visits
Calculate the number of days since last MD office visit (count days between
office visit encounters).
Report count
15. New Rx w/o office visit (not
refills)
Calculate the number of new prescriptions without an office visit. Assign
score 70%=10,50% = 8, 25%=5, 10-24%=3.
10
21
Drug Seeking Behavior
Drug Seeking Behavior: Patient #1 graphed CII’s
Days supply
prescribed
Weekly
22
Drug Seeking Behavior: Patient #2 graphed CII’s
National Special Investigations Unit (NSIU)
46 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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NSIU and Hotline Operations Org Chart
Marita Janiga
Chanelle Gamble
Administrative Specialist
Denise Barrow
NSIU Compliance Consultant
National Director of
Fraud Control & NSIU
Barbara Naimark
NSIU Manager
Roianne Summers
Topaiz Bernard
Senior Manager Compliance Hotline
Operations
Manager Compliance Hotline Training &
Policy Administration
Kathy Robertson
SCAL, Northwest
Colorado, Georgia,
Ohio, MAS
NCAL, Hawaii, Program Office
Compliance Consultant III
Ruby Moon
Compliance Consultant III
Rick Germroth
Bruce Burroughs
Investigations Manager
Investigations Manager
Investigations Manager
Glenn Prinsze
Barb Hudspeth
Linda Brown
Senior Investigator
Sr. Medical
Investigator
David MacLeod
Brandy Cannon
Senior Investigator
Senior Investigator
Senior Investigator
John Farquhar
Senior Investigator
47 April 29, 2012
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Daniel Falzon
Dale Bird
Joaquin Basauri
Senior Investigator (Sacramento)
Wendy Swallow
Compliance Consultant II
Stacy MacCready
Compliance Consultant III
Rick Green
Compliance Consultant II
Jasmine Williams
Compliance Consultant II
Senior Investigator
Vacant
Senior Investigator
Vacant
Senior Investigator
© 2012 Kaiser Foundation Health Plan, Inc.
Three Sources for Prescription Drug Abuse Data
Drug Enforcement
Administration (DEA)
 Drugs of Abuse –
2011 Edition
Resource Guide
 2011 Drugs of
Concern
 Bath Salts
 Salvia
 Dextromethorphan
(DXM)
48 April 29, 2012
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National Institute
on Drug Abuse
National Association of
Drug Diversion Investigators
 Commonly Abused
Prescription Drugs –
Revised October
2011
 Frequently Abused
Prescription and
Over-the-Counter
Drugs (Identification
Chart) – Revised
March 2011
 Dextromethorphan
(DXM) replaces
Anabolic Steroids
 Dextromethorphan
Products (liquids and
tablet forms)
© 2012 Kaiser Foundation Health Plan, Inc.
24
U.S. Department of Justice
Drug Enforcement
Administration (DEA)
 More young Americans die from
drugs than suicides, firearms, or
school violence;
 The use of illicit drugs, and the nonmedical use of prescription drugs,
directly led to the death of 38,000
Americans in 2006, nearly as many
who died in automobile accidents;
 The only disease that affects more
people than substance abuse in
America today is heart disease;
 Substance abuse is the single
largest contributor to crime in the
United States;
 In the latest year measured, the
direct cost of drug abuse was
estimated at $52 billion, with indirect
costs of $128 billion.
49 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
National Institute on Drug Abuse (NIDA)
Types of prescription drugs (as identified by NIDA) that are commonly abused are
compared to data mining for detection of pharmacy losses at Kaiser Permanente.
50 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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National Association of Drug Diversion Investigators
Types of prescription drugs (identified by NADDI) that are commonly diverted are
compared to data mining for detection of pharmacy losses at Kaiser Permanente.
51 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
Epidemic:
Responding To America’s Prescription Drug Abuse Crisis
Prescription drug misuse and
abuse is a major public health
and public safety crisis.
Source: 2011 Prescription Drug Abuse
Prevention Plan
52 April 29, 2012
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© 2012 Kaiser Foundation Health Plan, Inc.
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Facts About Prescription
Drug Abuse
~7.0 Million Americans Reported Past-Month Use
of Rx Drugs for Nonmedical Purposes in 2010
In 2010, approximately 16 million
Americans reported using a
prescription drug for nonmedical
reasons in the past year; 7 million in
the past month.
Source: Office of Applied Studies, Substance Abuse
and Mental Health Services Administration, National
Survey on Drug Use and Health, 2010
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© 2012 Kaiser Foundation Health Plan, Inc.
Facts About Prescription
Drug Abuse, continued
After Marijuana, prescription and overthe-counter medications* account for
most of the commonly abused drugs.
Source: University of Michigan,
2010 Monitoring the Future Study
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© 2012 Kaiser Foundation Health Plan, Inc.
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NSIU Drug Diversion Investigations (by Year)
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© 2012 Kaiser Foundation Health Plan, Inc.
2011 Investigations by Region
All Cases vs. Substantiated Closed Cases
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© 2012 Kaiser Foundation Health Plan, Inc.
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Top Five Drugs Listed in 2011 Investigations
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© 2012 Kaiser Foundation Health Plan, Inc.
Chemical Compounds and their Brand name Equivalents
Hydrocodone
bitartrate and
acetaminophen
• Hydrocodone
• Norco
• Vicodin
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Oxycodone
hydrochloride
Promethazine with
codeine
•
•
•
•
• Phenergan w/Codeine
• Promenthazine
w/Codeine
Oxycodone
OxyContin
Roxicodone
Roxicet
Hydromorphone
Benzodiazepine
• Dilaudid
•
•
•
•
•
Diazepam (Valium)
Lorazepam
Temazepam
Xanax
Ativant
© 2012 Kaiser Foundation Health Plan, Inc.
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Subjects in Substantiated 2011 Drug Diversion Cases
0
1
2
3
4
5
6
7
8
Patient/Member
Pharmacy Clerk/Assistant
Pharmacy Technician
Registered Nurse
Phamacist
Medical Assistant
Orthopedic Technician
Department Manager
Dentist
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© 2012 Kaiser Foundation Health Plan, Inc.
Identity Theft and Doctor Shopping
 Non-Member using Emergency Room and Urgent Care
Services
 Kaiser Permanente facilities
 Other area hospitals and clinics
 Chief complaint Sickle Cell Anemia pain
 Staff recognized patient using different name and birth date
from previous visits
 Sacramento Police arrested subject in hospital
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© 2012 Kaiser Foundation Health Plan, Inc.
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Data Mining Matrix Based on
Commonalties
 Name Similarity
 Phone Similarity
 Address Similarity
 Nineteen (19) Aliases
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© 2012 Kaiser Foundation Health Plan, Inc.
Using Multiple Identifications
Patient Name
RB
RB
AB
AB
AB
SB
TB
RB2
RB2
RB3
RB3
RB4
TD
TD
TD
JG
JG
RG
TJ
RJ
RJ
RJ
RJ
RJ
62 April 29, 2012
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Date of Birth
5/29/1987
5/29/1981
1/12/1988
1/12/1988
5/30/1988
5/29/1988
5/29/1988
5/29/1988
5/29/1988
5/29/1988
5/29/1988
5/29/1988
1/12/1988
1/12/1988
1/12/1988
3/29/1989
3/29/1989
5/29/1984
5/29/1989
1/4/1989
6/4/1989
6/4/1989
6/4/1989
6/4/1989
Date Filled: Date
Month &
Filled:
Day
Year
18-Oct
2006
15-Feb
2009
7-Feb
2008
7-Feb
2008
24-Mar
2008
10-Jan
2008
10-Apr
2008
6-May
2008
6-May
2008
16-Dec
2007
28-Dec
2007
7-Apr
2008
20-Jan
2008
22-Jan
2008
25-Jan
2008
25-Aug
2008
25-Aug
2008
24-Dec
2008
11-Jul
2009
22-Feb
2007
01//09
2008
11-Mar
2008
12-Mar
2008
21-Mar
2008
Drug Name
APAP / Hydrocodone
Hydromorphone HCL
APAP / Oxycodone
Zolpidem Tartrate
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Hydrocodone
Diazepam
APAP / Oxycodone
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Oxycodone
Diazepam
Methadone HCL
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Hydrocodone
APAP / Oxycodone
APAP / Hydrocodone
APAP / Hydrocodone
Quantity
30
40
30
30
30
20
40
30
15
20
10
20
30
20
20
43
15
30
30
30
10
20
25
30
Pharmacy
Rite Aid
Thrifty Payless
Kaiser Morse
Kaiser Morse
Kaiser Morse
Wal-mart
Rite Aid
Walgreens
Rite Aid
Kaiser Morse
Kaiser Morse
Kaiser Morse
Kaiser
Kaiser
Kaiser
Kaiser Morse
Kaiser Morse
Mercy
Walgreen
Walgreens
Rite Aid
Rite Aid
Walgreens
Kaiser Morse
M.D
Dr. L
Dr. M
Dr. V
Dr. J
Dr. H
Dr. Y
Dr. G
Dr. H2
Dr. S
Dr. N
Dr. S2
Dr. W
Dr. G
Dr. G
Dr. G2
Dr. H
Dr. O
Dr. W2
Dr. C
Dr. S3
Dr. B
Dr. V2
Dr. H2
Dr. N
© 2012 Kaiser Foundation Health Plan, Inc.
31
Loss / Cost of Fraudulent Care
MJ2
RB
MB
JG
AB
AB2
RB2
TD
RG
RJ
RJ2
TJ
TJ2
TJ3
AM
BS
TS
DT
Total Charges
63 April 29, 2012
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$16,261.20
$75,954.45
$32,040.05
$19,470.05
$24,925.00
$7,993.50
$14,490.00
$6,220.00
$576.15
$42,286.45
$25,577.00
$150.00
$5,825.00
$5,990.00
$9,325.00
$6,101.15
$11,045.00
$6,247.50
$337,533.35
© 2012 Kaiser Foundation Health Plan, Inc.
Outcome
Conviction
– Three counts of making fraudulent claims for health care benefit
payments
– Ordered to pay restitution to hospitals victimized
– Sentenced 7 years state prison
Deputy District Attorney stated, “Using fraudulent information to
feed a pharmaceutical addiction at the taxpayer’s expense is a
serious crime. Today’s sentence is a just consequence given the
impact fraud has on its victims and our community.”
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Soma Variance Report
•July 2011, Southern California Pharmacy
•Pharmacy Data Mining identifies potential
diversion pattern for Carisprodol (Soma®)
•Daily pill counts are initiated on July 19,
2011
•Eleven tablets missing July 20
•One hundred tablets missing July 25
•Seventy tablets missing July 29
•It was later validated that two other reported incidents
were actually “Return to Stock”
•Covert cameras were installed
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© 2012 Kaiser Foundation Health Plan, Inc.
Soma Variance, continued
• August 29, Pharmacy Assistant captured on Video removing a
bottle of Soma from the bin
• When interviewed, Assistant claimed to “not follow the rules” regarding
pulling medication but insisted “the pharmacists aren’t following the
rules either”
• After conferencing with the Union rep, the story changed to an “unnamed patient” receiving the pills
• Other Pharmacy Assistants were implicated as being responsible for
the missing drugs
• Investigation continued
• September 12, Pharmacy Assistant re-interviewed and
continued to deny taking the pills in spite of video evidence
• Employment terminated October 5, 2011
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Drug Abuse in the United States
“The negative impacts of substance abuse
span a broad spectrum, including health
care costs, public safety, economic
development and social services.”
– Dr. Regina M. Benjamin, US Surgeon General
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Background
 Early Spring 2008, NSIU began seeing an ID theft trend with
Kaiser Permanente employees – Radiation techs in one
particular medical center.
– Several TrakWeb cases opened involving Kaiser Permanente victims
who reported the theft of their identities
 In late Spring, NSIU received a fraud alert from Chase that
someone was opening credit card accounts using Kaiser
Permanente’s Human Resource Information System’s (HRIS)
800 number as the callback number
 All victims were Northern California employees
 NSIU, iACT and Information Technology began focusing on a
possible leak / breach in the Human Resource Service Center
(HRSC)
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34
Chase Alert
 About 50 Kaiser Permanente employees’ names were on the
fraud alert
 Several of the employees on the alert had previously called NSIU
to report ID theft and cases were opened
 Data from Chase made it clear the subject was not likely working
at one facility – clusters told us that
 Began data mining with HRSC data relative to the Chase list
 No anomalies or commonalities were found in the
HRIS data
 We were at a dead end
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© 2012 Kaiser Foundation Health Plan, Inc.
The Big Break
 January 27, 2009 – NSIU received call from a local Police
Department
 December 23, 2008 – Mia Garza arrested following a probation
search conducted at her apartment
 During the search, evidence was observed indicating that Garza
was in possession of stolen property as well as fake
identification.
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© 2012 Kaiser Foundation Health Plan, Inc.
35
Data Mining
January 28, 2009
iACT is asked for forensic data
mining assistance by NSIU and
Northern California leaders.
GOAL: To help locate data breach
source associated with the “Garza
list” (list found by police with data
for over 29 thousand apparent
Northern CA employees).
STARTING POINT:
Find similarities and differences between data
on the “Garza list” and HRIS and payroll
systems data to zero in on when the “Garza
list” data was last accurate or changed.
MOVING IN:
iACT compared wage rates and names on the
“Garza list” to HR systems to find when data on the
list was accurate and analyzed variances.
DRILLING DOWN:
At this point it appeared that
the “Garza list” contained
names of more than 29,000
people who had been
employed by Kaiser
Permanente at some fairly
recent point in time.
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April 29, 2012
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By identifying when changes occurred that were not
on the “Garza list” but that were updated in the HR
systems data, iACT moved quickly to narrow down
the date range for creation of the Garza list.
WHO IS ON THE LIST?
After removing 45 duplicate
names, iACT discovered
that all names on the
“Garza list”, except a
handful, were presently in
payroll systems.
NEXT STEPS:
iACT then analyzed current data from
our HR systems, compared to the
“Garza list.”
iACT added demographic information
from other HR systems, including
workplace information and union
affiliation, if any.
Ask us why!
© 2012 Kaiser Foundation Health Plan, Inc.
Found It!
 In coordination with NSIU & Human Resource partners, iACT
was able to isolate one file produced by the HRIS System that
contained all elements found on the “Garza list.”
 That file was one that Kaiser Permanente , Human Resource
Service Center sent outside to United Healthcare Worker’s West electronically via FTP (File Transfer Protocol) every other
Sunday.
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Be Well and …
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