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Healthcare Law Section MCLE Meeting
DuPage Judicial Center - ARC
September 30, 2019
11:45 AM – 12:00 PM Welcome/Introductions
Jay Bogdan – Healthcare Law Section Member
12:00 PM – 1:00 PM Program – Deconstructing Healthcare Fraud
Stuart G. Berman, CFE, CAMS, Fernanda Gomes and Enjie Zong -
Capital Forensics, Inc.
Speakers’ Bios - See attached
Presentation Summary
The presentation explores the breadth of health care fraud and costs
associated with schemes perpetrated by both patients and providers.
In addition, modern investigative techniques including data analytics
to uncover coding frauds will be detailed.
Next Meeting: TBD
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Arboretum
November 15th & 16th – GAL Training – 421 Building - Auditorium
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1530 E. Dundee Road, Suite 333 | Palatine, IL 60074 | T 888-970-1700 | P 847-392-0900
Stuart Berman began his career as a Securities Enforcement Auditor with the
Illinois Securities Department. As a Securities Enforcement Auditor, he
specialized in detecting promissory note Ponzi schemes and worked on several
multimillion dollar schemes.
Berman used this experience toward what would be a long and successful
career as a Special Agent with the U.S. General Services Administration (GSA) Office of Inspector
General (OIG) in Chicago. In that role, he planned, organized and conducted complex white-collar
investigations concerning GSA contracts, personnel, contractors and contractor employees. These
investigations involved a wide variety of fraud allegations, including bribery, kickbacks, inferior
quality-product substitution, cost mischarging, anti-trust violations, credit card fraud, diversion of
excess government property and money laundering.
Berman became the Special Agent In Charge for the GSA OIG in August 2008, and supervised
the GSA OIG regional criminal, civil and administrative investigative program, encompassing six
Midwestern states and 12 judicial districts. He was responsible for managing investigative business
operations and program functions as well as administrative operations, including budget and
personnel decisions. Berman provided technical advice, counsel and support to secondary
management teams and to the criminal investigative and administrative support units. After a
highly successful and award-winning law enforcement career, Berman retired from the GSA OIG
at the end of April 2018.
Prior to Capital Forensics, Inc. (CFI), Berman served as the Chicago Practice Lead and Director
of Forensics & Valuation Services for a national accounting and advisory firm. In July 2019,
Berman joined CFI as a Director of Risk Management and Investigations. CFI is an expertise-led,
independent, forensic accounting, consulting and data analytics firm, dedicated to helping
organizations investigate and prevent corruption issues, manage change, mitigate risk and resolve
disputes. CFI provides a full range of services involving litigation support, including expert
testimony, securities litigation support, and mock arbitrations, along with corporate investigations,
business interruption claims, money laundering investigations, anti-corruption/anti-fraud
consulting, risk assessments, and big data analytics.
Berman is a Certified Fraud Examiner (CFE) and a Certified Anti-Money Laundering Specialist
(CAMS). He is a summa cum laude graduate of DePaul University, Chicago, with a B.A. degree.
1530 E. Dundee Road, Suite 333 | Palatine, IL 60074 | T 888-970-1700 | P 847-392-0900
Fernanda Gomes started her career as a Business Consultant with Deloitte, and has grown into a Subject Matter Expert in Data Analytics, leveraging specialization in risk management, internal audit, project management and process design.
She bridges gaps between technical and non-technical departments with deep knowledge of both the business processes and underlying technology for innovative solutions, methodology implementation, and organizational effectiveness.
She employs a techno-functional approach and strategic vision to heighten efficiencies and improve manageability in varied environments. She consults cross-functional, peak-performing teams to achieve business objectives with customized solutions.
• Skilled at determining the feasibility, impact, risks, and profit/growth potential of automated programs, next-generation technologies and data science practices.
• Strong people leader excelling at building, developing, and mentoring teams in providing high-quality, meaningful intelligence to senior management and other stakeholders for short- and long-term results.
• Experienced on strengthening processes, methodologies, and standards to increase the effectiveness and value data management and information strategies.
Fernanda is certified in Data Science by Johns Hopkins University Specialization on Coursera, and has extensive experience applying advanced analytics to solve business problems and challenges.
1530 E. Dundee Road, Suite 333 | Palatine, IL 60074 | T 888-970-1700 | P 847-392-0900
Enjie Zong Financial Data Analyst
Enjie (“N.J.”) Zong is a Financial Data Analyst at Capital Forensics, Inc. Enjie provides quantitative analysis on a range of forensic cases, and is responsible for developing data analytics automation solutions, modeling to represent investment portfolio performance, and designing data visualization reports. With the use of advanced analytic techniques including Regular Expression, PHP Scripting, and Machine Learning, Enjie is able to capture, manage and process structured, semi-structured and unstructured data sets, from different sources, and in different sizes. Prior to his role as a Financial Data Analyst, Enjie worked as a Forensic Accountant, responsible for quantitative analysis and qualitative investigation on employee theft/employee dishonesty as well as other assets misappropriation insurance claims. Enjie holds a Master’s of Science in Finance from Loyola University Chicago Quinlan School of Business. He also received both a Bachelor of Arts in Accounting and a Master's of Accountancy from the University of Illinois at Springfield.
De-constructing Healthcare Fraud:
Patients and Providers
Stuart Berman, CFE, CAMS
Enjie Zong
Fernanda Gomes
Presented By:
2019
Agenda
Introduction
Health Care Violations
Fraud V. Abuse
Detection and Data Analytics
Fraud Profiles and Schemes
The Problem Illustrated
The U.S. spent approximately 3.5 trillion on healthcare in 2017, which equates to $10,739 per person. At least 3 percent of that spending or 105 billion is lost to fraud each year.
(National Health Expenditure Accounts, 2017)
The 2016 amount of improper payments is estimated at $51.9 billion for Medicare, $1.2 billion for CHIP, and$36.7 billion for Medicaid. Improper payments for all 19 programs totaled $134.6 billion.
(www.paymentaccuracy.gov)
Medicare paid dead physicians 478,500 claims totaling up to 92 million from 2000 to 2007. These claims included 16,548 to 18,240 deceased physicians.
(U.S. Senate Permanent Committee on Investigations, 2008)
Who Interprets and Enforces the Fraud and Abuse Laws?
Office of Inspector General, Department of Health
and Human Services (OIG)
Prosecutors
Private whistleblowers and their counsel
Courts, Agencies, Manuals, Treatises
Compliance Officers, Attorneys, Professional Societies
Center for Medicare and Medicaid Services (CMS)
Government Contractors
5 most important Federal fraud and abuse laws that apply to physicians
➢ False Claims Act (FCA)➢ Anti-Kickback Statute (AKS)➢ Physician Self-Referral Law (Stark Law)➢ Exclusion Statute➢ Civil Monetary Penalties Law (CMPL)
False Claims Act [31 USC §§ 3729-3733]
Purpose: Protect the Government from harm
• Illegal to knowingly submit false or fraudulent claims to
Medicare or Medicaid for payment
• False claims: Could result in fines of up to three times the
programs’ loss plus an additional $11,000 per claim.
• Under the CFCA, no specific intent is required
• Whistleblower may file lawsuit on behalf of U.S., and may be
entitled to a percentage of recoveries
Criminal False Claims Act (18 U.S.C. § 287)
Purpose: Criminal Penalties for Submitting False Claims
Whoever makes or presents to any person or officer in the
civil, military, or naval service of the United States, or to any
department or agency thereof, any claim upon or against the
United States, or any department or agency thereof, knowing
such claim to be false, fictitious, or fraudulent, shall be
imprisoned not more than five years and shall be subject to a
fine in the amount provided in this title.
Anti-Kickback Statute [42 U.S.C. § 1320a-7b(b)]
Purpose: Criminal law that prohibits the knowing and willful payment
of referrals
• In Federal Health Care, paying for referrals is a
crime.
• Payers and recipients: Those who solicit or receive
remuneration.
• Remuneration can be anything of value including
cash, free rent, hotel stays, meals, and excessive
compensation for medical directorships and
consultancies.
Anti-Kickback Statute [42 U.S.C. § 1320a-7b(b)]
Purpose: Criminal law that prohibits the knowing and willful payment
of referrals
• Waiving copays from patients generally is a violation
• Accepting money or gifts from a drug or device
company is not justified by the articulation that the
ordered treatment would have been prescribed,
nonetheless.• There is no onus on the Government to prove harm to the
patient or program loss.
Physician Self- Referral Law [42 U.S.C. § 1395nn]This is also referred to as the “Stark Law”
Purpose: Prohibits physicians from referring patients to receive
“designated health services” payable by Medicare or Medicaid from
entities with which the physician has a financial relationship
Designated Health Services:• Clinical laboratory services• Physical therapy, occupational
therapy, and outpatient speech-language pathology services
Strict liability statute: Does not require proof of specific intent.
Penalties: Fines and exclusion from participation in the Federal health care programs.
Physician Self- Referral Law [42 U.S.C. § 1395nn]Stark Law
Designated Health Services:
• Radiology and certain other imaging services
• Radiation therapy services
• Durable medical equipment and supplies
• Parenteral and enteral nutrients, equipment and supplies
• Prosthetics
• Home health services
• Outpatient prescription drugs
• Inpatient and outpatient hospital services
Exclusion Statute [42 U.S.C. § 1320a-7]Purpose: Exclusion from participation in all Federal health care programs
The OIG excludes individuals and entities convicted of the following types of criminal offenses:
(1) Medicare or Medicaid fraud, as well as any other offenses related to the delivery of items or
services under Medicare or Medicaid;
(2) patient abuse or neglect;
(3) felony convictions for other health-care-related fraud, theft, or other financial misconduct; and(4) felony convictions for unlawful manufacture, distribution, prescription, or dispensing of
controlled substances. OIG has discretion to exclude individuals and entities on several other
grounds, including misdemeanor convictions related to health care fraud other than Medicare
or Medicaid fraud or misdemeanor convictions in connection with the unlawful manufacture,
distribution, prescription, or dispensing of controlled substances; suspension, revocation, or surrender of a license to provide health care for reasons bearing on professional competence,
professional performance, or financial integrity; provision of unnecessary or substandard
services; submission of false or fraudulent claims to a Federal health care program; engaging
in unlawful kickback arrangements; and defaulting on health education loan or scholarship
obligations.
Exclusion Statute [42 U.S.C. § 1320a-7]Purpose: Exclusion from participation in all Federal health care programs
OIG prohibitions:
Excluded physicians may not bill directly for treating Medicare and Medicaid patients, nor
may their services be billed indirectly through an employer or a group practice.
In addition, if you furnish services to a patient on a private-pay basis, no order or
prescription that you give to that patient will be reimbursable by any Federal health care
program.
http://oig.hhs.gov/fraud/exclusions.asp.
Civil Monetary Penalties Law [42 U.S.C. § 1320a-7a]Purpose: OIG may seek civil monetary penalties and sometimes exclusion for a wide variety of conduct and
is authorized to seek different amounts of penalties and assessments based on the type of violation at issue
Penalties range from $10,000 to $50,000 per violation:
❖ presenting a claim that the person knows or should know is for an item or service that was not
provided as claimed or is false or fraudulent;
❖ presenting a claim that the person knows or should know is for an item or service for which payment
may not be made;
❖ violating the AKS;❖ violating Medicare assignment provisions;
❖ violating the Medicare physician agreement;
❖ providing false or misleading information expected to influence a decision to discharge;
❖ failing to provide an adequate medical screening examination for patients who present to a hospital
emergency department with an emergency medical condition or in labor; and❖ making false statements or misrepresentations on applications or contracts to participate in the
Federal health care programs.
“Fraud” is intentional breach of the standard of good faith and fair dealing, as
understood in the community, involving deception or breach of trust, for money.
(USA v. Goldblatt)
DEFINITION
In Health Care Practice, “Fraud And Abuse” has come to include far more than the Goldblatt definition-kickbacks and Stark Violations.01.
Health Care Fraud, Mail Fraud, False Statements and Instruments, Insurance Fraud Statutes02. 42 CFR 433.302 definition of Fraud for Medicaid04
.
“False or Fraudulent Claim” in False Claims Acts(State and Federal 31 U.S.C. 3729 et seq.)03
.
What is Fraud?
What is
“Abuse means practices that are
inconsistent with sound . . . medical
or professional practices and which
result in unnecessary costs. . .,
Payment for services not medically
necessary, or . . .Which fail to meet
recognized standards for health
care.”
17
Fraud
01. “Fraud” requires evidence of the intent of a specific individual
02. “Fraud means an intentional deception or misrepresentation made by a person with the knowledge that the deception could result in some unauthorized benefit to himself or some other person. It includes any act that constitutes fraud under applicable Federal or State law. “ 42 CFR 455.2
How Does “Fraud” differ from
“Abuse”?
18
Abuse
03. “Abuse means provider practices that are inconsistent with sound fiscal, business, or medical practices, and result in an unnecessary cost to the Medicaid program, or in reimbursement for services that are not medically necessary or that fail to meet professionally recognized standards for health care. It also includes recipient practices that result in unnecessary cost to the Medicaid program.” 42 CFR 455.2-similar provisions in state regulations-(see, e.g., 18 NYCRR 515.1 (B))
04. No evidence of intent of specific individual required
How Does “Fraud” differ
from “Abuse”?
05. At the beginning of an investigation, neither prosecution nor defense can know whether matter will be a fraud case, an abuse case, or no case.
19
Profile of the Typical
Fraud Perpetrator
FRAUD PERPETRATOR
Profile of a
Only 5%No Prior Criminal History
Well Liked by Co-workers
Likes to Give Gifts/Compulsive Shopper
Gambling Problems(Not Unusual)
Rationalizes, Starts Small or “Borrows”
Long-term Employee
Lifestyle Clues
©2018 Association of Certified Fraud Examiners, Inc.
The Typical Perpetrator
W H I T E M A L E
C O L L E G E - E D U C AT E D
I N T E L L I G E N T
M A R R I E D
M O S T L O Y A L E M P L O Y E E
The Fraud Triangle
Person’s Rationalization or Integrity
Perceived Opportunity to Commit FraudPerceived Pressure Facing Individuals
Exacerbated in
Economic
Downturn
By Donald R. Cressey, Ph.D.
Which one looksSuspicious?
$3M $4.6M $1.0M
$31M $900,000 $1.1M
Fraud Examples (If Conduct Is Intentional)
Billing for services not provided
Billing for unneeded services
Submitting a claim you know is not reimbursable (stark)
Submitting a claim you know is not properly coded to get more money
Providing a false diagnosis
Keeping money for which you know you are not entitled
Creating false documents to support a claim or cost report
What Kinds of Practitioner
Schemes Exist?
•
•
•
•
•
•
•
•
•
•
•
C o n t i n u e d
Use of unlicensed staff
Providing unnecessary services or ordering unnecessary tests
Waiving member co-pays
Kickbacks
Practitioner Schemes
01. Computerized Inclinometry
02. Nerve Conduction Studies
03. Surface Electromyography
04. Ultrasound Screening
05. Unnecessary X-Rays
Both standard and nonstandard - appears to be
much more common among chiropractors and
joint chiropractic/medical practices than among
other health-care providers.
Billing For Inappropriate Tests
$1 in $7 Billion is attributed
to fraud in MedicareUnited
States
01 Men having babies
02 Fillings in crowns
03 Excluded persons on public, health provider payroll
04 Hospital in-patient ambulance trips
05 Women giving birth every 5 months
06 Dead people having prescriptions filled
07 Enrollees with multiple lives
08 One enrollee-two managed care numbers
Data Matches/Demographics
How Can “Insured Members" Commit Health Care Fraud?
1. Providing false information when applying for programs or services
2. Forging or selling prescription drugs
3. Individuals obtaining subsidized or fully-covered prescription pills that are not needed and then selling them on the black market for a profit
4. Using transportation benefits for non-medical related purposes
5. Loaning or using another’s insurance card
Insured Member Schemes
Traditional Model
Informants
Whistleblowers
Hotlines
Reading Newspapers
Health systems have a growing
focus on analytics for the future
Data Analytics &
ArtificialIntelligence
BIG DATA
Information of extreme size, diversity and complexity.
DEFINITIONS
DATA ANALYTICS
…processes and activities designed to obtain and evaluate data to extract useful information and answer strategic questions...
Source: http://www.gartner.com/technology/topics/big-data.jsp
31
Big Data
Unstructured data example:We can extract meaning and intent from visual elements, whether characters (digitalized documents) or categorization of content in images.
Unstructured data example:Let’s take a look at a sample medical bill.
It is the conjunction of structured , such as tables, and unstructured data including images, audio, videos, and text; collected in real time from diverse sources
• Brains process visuals up to 60,000 times faster than
text alone
• Speed-up the data understanding process, as the
volume of data available is increasing constantly
• Increase audience engagement and understanding
Data Visualization
Machine LearningMethods used to automate the building of analytical models. Based on statistical techniques, the algorithm is created and continues to learn and improve
Need attention
Suspicious
The machine can learn from you: based on fraudulent
transactions known, the algorithms can flag similar
transactions
Supervised Learning
Anomaly/outliers
And it can learn for you: machine can look for
patterns that are not expected, bringing to light
unknown issues
Unsupervised Learning
In order to demonstrate the power of the technology, we are going to present a case study based on the HealthCare Synthetic Data
made available by the Center for Medicaid and Medicare Services (CMS) .
The Medicare/Mediclaim data provides a large number of inpatient claim records (Synthetic).
A claim consists of a set of medical codes:
• Beneficiaries information: Beneficiary Birth Date, Sex Identifier, Race, etc.
• Claim details: Claim ID, Beneficiary ID, Physician ID, Diagnosis Code, Medicare Payment Amount, Deductible Amounts, etc.
• Diagnosis, Drugs and Procedures: Codes and Description.
One way a service provider could defraud the insurance company is to "accidentally" drop in high-priced procedure codes that don't
"belong".
We built a model to find patterns of a “normal” claim and flag the “abnormal” claims.
Case StudyCenter for Medicaid and Medicare Services (CMS) 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF)
T E X T UA L A N A LY T I C S
BETWEENNESS
CENTRALITY
22%
At Work
Textual Analytics
At Work
Textual Analytics
From: MF3/19/2010
To: ES
Subject:
I am happy and distraught. Happy for you, distraught for me :) Who will take care of me?? I will be adrift and unloved. Seriously, I hope that you are happy at Company A and you already know you can work for Ryan so it should be a smooth transition. I just want you to know how much I truly appreciated everything you have done for me and my team.
You will be missed...
E-mails
It Can Even Catch
CONTACT US
GET IN TOUCH
1530 E. Dundee Rd.
Suite 333
Palatine, IL 60074
ADDRESS
847.392.0900
PHONE
Stuart Berman, CFE, CAMS
Enjie Zong
Fernanda Gomes
