Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Health Care Compliance Institute Data Mining Workshop (P17) Marita Janiga, Director, National Special Investigations Unit Jay Loden, Assistant Director, Data Analytics April 29, 2012 | © 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 | © 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 | © 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 | © 2012 Kaiser Foundation Health Plan, Inc. 2 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 | 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 | © 2012 Kaiser Foundation Health Plan, Inc. 3 Information Analytics Compliance and Technology (iACT) 7 April 29, 2012 | © 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 | © 2012 Kaiser Foundation Health Plan, Inc. 4 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 | © 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 | Network Link/ Neural Analysis • discovery through associative links, usually hidden layers below common data © 2012 Kaiser Foundation Health Plan, Inc. 5 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 11 April 29, 2012 | © 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 12 April 29, 2012 | Pharmacy Audit & Compliance © 2012 Kaiser Foundation Health Plan, Inc. 6 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 | © 2012 Kaiser Foundation Health Plan, Inc. FWA Program Total Recoveries and Avoidances 14 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 7 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 • • 15 April 29, 2012 | © 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 16 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 8 Pharmacy Data Warehouse Volume 17 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. Pharmacy Data Warehouse Volume 18 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 9 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 20 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 10 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 | © 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. 22 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 11 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 | © 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 24 April 29, 2012 | 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 25 April 29, 2012 | © 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 | Computer power! 700 / 3 = 233 Total studies produced monthly 233x400 = 93,200 © 2012 Kaiser Foundation Health Plan, Inc. 13 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 | © 2012 Kaiser Foundation Health Plan, Inc. Total $$’s Billed Physician Billings vs. Visits: Outlier Analysis # of Visits 28 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 14 Outlier Analysis Total $$’s Billed Bad guy! # of Visits 29 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. Raw Data ~ Summary Output 30 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 15 Raw Data - Graphed Variance Report – Viagra 100 mg Tab Diversion Begins 31 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. One Touch Ultra Test Strips, cont. Baseline data from a similar pharmacy Pharmacy Comparison 32 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 16 Drug Diversion Case: One Touch Ultra Test Strips Diabetic Strips Diabetic Test Test Strips 10/12/08 Transfers away from Pharmacy 33 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. One Touch Ultra Diabetic Test Strips 10/13/08 Transfers into New Pharmacy 34 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 17 One Touch Ultra Test Strips, cont Law Enforcement provided NSIU with Pay Pal Transaction Data 35 April 29, 2012 | © 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 36 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 18 Prescribing Patterns / Script Mills 37 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. Prescribing Patterns / Script Mills 38 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 19 Prescribing Patterns / Script Mills 39 April 29, 2012 | © 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 | © 2012 Kaiser Foundation Health Plan, Inc. 20 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 | © 2012 Kaiser Foundation Health Plan, Inc. 23 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 | 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 | 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 | © 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 | © 2012 Kaiser Foundation Health Plan, Inc. 25 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 | © 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 | © 2012 Kaiser Foundation Health Plan, Inc. 26 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 53 April 29, 2012 | © 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 54 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 27 NSIU Drug Diversion Investigations (by Year) 55 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 2011 Investigations by Region All Cases vs. Substantiated Closed Cases 56 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 28 Top Five Drugs Listed in 2011 Investigations 57 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. Chemical Compounds and their Brand name Equivalents Hydrocodone bitartrate and acetaminophen • Hydrocodone • Norco • Vicodin 58 April 29, 2012 | 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. 29 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 59 April 29, 2012 | © 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 60 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 30 Data Mining Matrix Based on Commonalties Name Similarity Phone Similarity Address Similarity Nineteen (19) Aliases 61 April 29, 2012 | © 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 | 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 | $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.” 64 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 32 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 65 April 29, 2012 | © 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 66 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 33 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 67 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 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) 68 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 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 69 April 29, 2012 | © 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. 70 April 29, 2012 | © 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. 71 April 29, 2012 | 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. 72 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 36 Be Well and … 73 April 29, 2012 | © 2012 Kaiser Foundation Health Plan, Inc. 37