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Medical fraud and its implications Dr Vaikuthan Rajaratnam
1. Medical fraud and its implications
Dr Vaikunthan Rajaratnam
MBBS(Mal),AM(Mal),FRCS(Ed),FRCS(Glasg),FICS(USA),MBA(USA), Dip Hand Surgery(Eur),
Dip MedEd(Dundee),FHEA(UK),FFSTEd,FAcadMEd(UK)
Senior Consultant Hand Surgeon, KTPH Alexandra Health,
Honorary Senior Lecturer, YYL School of Medicine, National University of Singapore,
Core Faculty for Orthopaedic Surgery and Hand and Reconstructive Micro Surgery,
NHG Residency Program , SINGAPORE
2. Healthcare expenditure lost to fraud annually
Global estimate
US$415 billion
(~1.3 trillion MYR)
Europe
56 billion euros
(~240 billion MYR)
Source: European Healthcare Fraud & Corruption Network
3. Annual cost of medical fraud
Proportion of healthcare expenditure
lost to fraud or error not known
Estimate:
At least 3%, probably more than 7% and
possibly as much as 10%
Source: The Financial Cost of Healthcare Fraud 2011 Report, PKF (UK) LLP and
University of Portsmouth
4.
5. Medical Fraud Estimate Malaysia
• Health care is 4.75 % of 303.5B = 14.4B
• Medical fraud estimated at 3% - 10%
• US$ 0.4b to 1.4b or
7. Implications of medical fraud
“Fighting fraud in healthcare is the first
and most effective step for governments
and for private insurers when setting up
cost cutting strategies in order to stop
losses without reducing the access to and
the quality of care.”
Paul Vincke
President
European Healthcare Fraud and
Corruption Network
8. What is medical fraud?
Fraud, waste or abuse of healthcare
resources/funds, regardless of whether
intent is proven (includes errors).
10. Types of medical fraud
Opportunistic Fraud
Commonplace
Low $/incident
Patients
Healthcare professionals
Healthcare managers &
staff
Professional Fraud
Less common
High $/incident
Organised criminals
Fraud by contractors & suppliers
High $/incident
Drug & equipment companies
11. Examples of opportunistic medical fraud
False claims by hospitals
to get extra payments
Using Government grants
for personal use
Managers submitting
false expenses claims
Patients lying about
financial status to get
free medical treatment
Health professionals
claiming for work that
has not been done
Patients pretending to
be residents of countries
to claim free treatment
12. Examples of professional medical fraud
Billings
Procedures
Quality
Prescriptions
Devices and Implants
were never provided
13. Examples of professional medical fraud
Bogus medical clinics set
up to bill insurers for
healthcare treatments
that were never provided
Use of stolen personal
identities to claim for
bogus procedures
Counterfeit drugs
and devices
14. Fraud by contractors or suppliers
2009: Pfizer Inc. was ordered to pay US$2.3
billion for misbranding medicines and paying
kickbacks to doctors
April 2013: US Government accuses Novartis of
paying multimillion-dollar kickbacks to doctors in
exchange for prescribing its drugs.
Drug companies in UK exploit loophole in the law
to hike prices by as much as 2,000%
2012 – 400 fake thermometers seized in the UK
15. Attorney General Eric Holder, who announced the $2.2 billion settlement with
Johnson & Johnson Monday, Nov. 4 in Washington, D.C. / CBSNEWS
16. Difficulties of detecting fraud
Acceptance of fraud by health payers
Health payers’ teams often work in silos
Hotlines and rules engines
Criminal dexterity
Costly pay and chase model
20. Solutions to detect & prevent fraud
Need different approaches which combine:
1) Knowledge of existing fraud schemes
2) Powerful predictive analysis techniques
3) Comprehensive triage and case management
capabilities.
21. Dynamic Rules Engines
Claims are run against a predefined set of
algorithms or business rules to detect known types
of fraud or abuse based on specific patterns of
activity, such as claims:
Exceeding certain amounts
Following changes to policies
For services inconsistent with medical history
22. Dynamic Rules Engines
Pros
Cons
Able to filter large volumes of
claims for further investigation
May uncover large numbers of
suspicious claims for further
investigation with many being
false positives
Fraudsters can easily learn the
rules and work around them
Rules are based on past fraud
experiences so unable to spot
new scams
Simple to set up and apply
23. Anomaly Detection
Report events that exceed a threshold for a particular
claims benchmark.
Pros
Cons
Outliers or anomalies could
indicate a new or previously
unknown pattern of fraud.
It can be difficult to determine
what to measure, what time
period to use and appropriate
threshold levels.
Straight forward, easy to
implement and intuitive
Fraudsters can easily learn the
rules and work around them
Rules are based on past fraud
experiences so unable to spot
new scams
24. Predictive Modeling
Uses data mining tools to build models to produce fraudpropensity scores.
Pros
Cons
Tends to be more accurate than
other fraud detection methods
Models degrade over time
Information is collected and crossreferenced from a variety of
sources providing a better balance
of data than rules-based systems.
Models need to be updated when
fraudsters come out with new scams
(statistical analysis can identify when
updates are needed).
Determines key metrics that are
associated with claims that have a
high fraud propensity score
25.
26. Social Network Analysis & Multi-Entity Fraud
Identifies links between entities to uncover abnormal
claims patterns.
Pros
Cons
Effective in identifying organized fraud
activities by modeling relationships
between entities in claims
Models degrade over time
and need updating when
fraudsters come up with
new scams.
Can be fully automated, with the system
continuously updating the interrelated
networks with new claims and rescoring
for fraud.
Large volumes of seemingly unrelated
claims can be checked, and then patterns
and problems identified.
27. Claims development process
• Investigates the claims and associated
documentation;
• Performs appropriate research regarding
liability, benefit categories, statutory
requirements, etc.;
• Determines if a payment error exists and the
nature of the error;
• Notifies the beneficiary and provider/supplier;
and
• Starts the payment reconciliation process.
28. selected target areas
•
•
•
•
•
High volume of services
High cost
Dramatic change in frequency of use
High risk problem-prone areas
Recovery Auditor
29. • minimize potential future losses to finaciers through
targeted claims review while using resources
efficiently and treating providers and beneficiaries
fairly
30. Restoring integrity in the medical profession
Professionalism
Accountability
Legislation
Probity
31. Sources & Suggested Reading
‘The Financial Cost of Healthcare Fraud 2011 Report’, PKF (UK) LLP
and University of Portsmouth
‘The Problem of Health Care Fraud’, 2009, National Health Care
Antifraud Association
‘Pfizer drug breach ends in biggest US crime fine’, 2 Sept 2009,
Andrew Clark, The Guardian
Justice Department Announces Largest Health Care Fraud Settlement
in its History, 2 Sept 2009, U.S. Dept of Health & Human Services
www.hhs.gov (U.S. Dept of Health & Human Services)
www.ehfcn.org (European Healthcare Fraud & Corruption Network)
www.nhcaa.org (National Health Care Anti-Fraud Association)
Notes de l'éditeur
Who pays? You and me. Insurance companies in the US lose nearly US$70 billion every year in medical fraud (National Health Care Antifraud Association, “The Problem of Health Care Fraud” 2009). Need to make up the losses so increase premiums. Hospitals charge more. Governments increase taxes. Counterfeit drugs and false medical devices can put patients’ lives at risk.
Who pays? You and me. Insurance companies in the US lose nearly US$70 billion every year in medical fraud. Need to make up the losses so increase premiums. Hospitals charge more. Government increases taxes.
Fraud is a legal term where intent must be proven for conviction. In this discussion the term medical fraud will cover fraud, waste or abuse of healthcare funds or resources including error as all affects the bottom line.Definition taken from: Combating Healthcare Fraud
1) Hospitals have been found to falsely claim that they have undertaken surgical procedures to attract extra payments. 2) Personal impropriety: One Chief Executive Officer of a healthcare organisation was found to have overclaimed on his mileage allowance by 55,000 miles.3) Dentists have been found to have claimed for dental work which has not been undertaken; to have claimed for gold fillings which were actually mostly composed of nickel; and to have claimed fees for re-opening their surgeries out of normal hours without actually doing this.4) Two doctors were found to have claimed a Government improvement grant for their surgery and to have subsequently spent the money on creating a car import/export business.
Professional fraud is often perpetrated by organized groups with multiple, false/stolen identities, targeting multiple organizations. These criminals know how fraud detection systems work, and they routinely test thresholds to stay just under the radar. These crime rings often place or groom insiders to help them defraud health payers through several channels at once.Criminals have been found to establish bogus medical clinics in order to bill insurers for healthcare treatments that were never provided and to have stolen confidential patient data for use in credit card fraud. UK 2007 – 70,000 packs of bogus drugs were imported and packaged to make them look like genuine medicines for prostate cancer (Casodex), heart conditions (Plavix) and schizophrenia (Zyprexa). They were passed to pharmacies, hospitals and care homes and at least 100,000 doses ended up being given to patients. http://www.mhra.gov.uk/NewsCentre/Pressreleases/CON155710 – 400 fake thermometers seized after the parents of a young child with leukaemia used a fake thermometer bought online and realised it was giving a misleading temperature reading. Their child had a high temperature and was rushed to hospital to receive urgent medical care despite the fake thermometer showing that their child did not have a high temperature
Professional fraud is often perpetrated by organized groups with multiple, false/stolen identities, targeting multiple organizations. These criminals know how fraud detection systems work, and they routinely test thresholds to stay just under the radar. These crime rings often place or groom insiders to help them defraud health payers through several channels at once.Criminals have been found to establish bogus medical clinics in order to bill insurers for healthcare treatments that were never provided and to have stolen confidential patient data for use in credit card fraud. UK 2007 – 70,000 packs of bogus drugs were imported and packaged to make them look like genuine medicines for prostate cancer (Casodex), heart conditions (Plavix) and schizophrenia (Zyprexa). They were passed to pharmacies, hospitals and care homes and at least 100,000 doses ended up being given to patients. http://www.mhra.gov.uk/NewsCentre/Pressreleases/CON155710 – 400 fake thermometers seized after the parents of a young child with leukaemia used a fake thermometer bought online and realised it was giving a misleading temperature reading. Their child had a high temperature and was rushed to hospital to receive urgent medical care despite the fake thermometer showing that their child did not have a high temperature
http://www.theguardian.com/business/2009/sep/02/pfizer-drugs-us-criminal-finehttp://www.hhs.gov/news/press/2009pres/09/20090902a.htmlPfizer case is the largest healthcare fraud settlement in history to date. Fine: $1.3 billion criminal fine and $1billion in a civil settlement which nearly all was returned to Medicare, Medicaid, and other government insurance programs to reimburse improper prescriptions. The fines are the culmination of a six-year investigation into Pfizer, sparked in part by a lawsuit filed by John Kopchinski, a Pfizer sales rep in Florida, who blew the whistle on what he called unethical conduct. Kopchinski, a Gulf War veteran, accused Pfizer of promoting Bextra for problems far wider than its approved uses, which were for treating arthritis and menstrual pain. He contended that this put patients at risk of heart attacks, strokes and blood clots.http://uk.reuters.com/article/2013/04/26/us-novartis-fraud-lawsuit-idUSBRE93P16120130426http://www.independent.co.uk/news/uk/politics/nhs-hit-for-millions-by-overcharging-scam-8708292.htmlThe controversial practice involves big-pharma firms selling on medicines commonly used by the NHS to businesses acting outside the Government’s price-regulation scheme. The purchasing firms are then free to mark up the prices they charge the NHS.http://www.mhra.gov.uk/NewsCentre/Pressreleases/CON155710 – 400 fake thermometers seized after the parents of a young child with leukaemia used a fake thermometer bought online and realised it was giving a misleading temperature reading. Their child had a high temperature and was rushed to hospital to receive urgent medical care despite the fake thermometer showing that their child did not have a high temperature
1) Acceptance – seen as inevitable so health payers accept a certain amount of fraud loss as a standard cost ofdoing business.2) Silos - health payers often operate with data systems and technical analysts that reside in silos, making it difficult or impossible for staff with expertise in legal investigation or clinical coding to assemble a complete view of claims history and member or provider data without assistance from other business units.3) Hotlines rely on tip-offs from the public. Rules engines look for claims that conform to previously identified fraud or abuse schemes, but fail to adapt to even slight modifications of those schemes, much less new schemes.4) Criminals – highly resourceful and adaptable. Focus on lots of mini activities that look legitimate in isolation e.g., recruitment and transport of patients for bogus procedures, trading narcotics in exchange for member IDs, identity theft, doctor and pharmacy shopping.5) Costly pay and chase – most cases investigations seek to recover payments that have already been made which requires legal action
Need more sophisticated methods to keep pace with fraudstersRapid advances in technology enable insurance companies to use more powerful techniques to not only detect fraudulent activity, but to prevent it. This reduces losses and saves money on cost recovery.Need a combined approach to create a system that can: adapt continuously to evolving fraud and abuse schemes.• Offer prepayment detection of suspicious claims with high certainty.• Provide enough efficiency to enable triage of large volumes of claims.• Automate the detection of multi-entity fraud and abuse schemes.
These test each transaction against a predefined set of algorithms or business rules to detect known types of fraud or abuse based on specific patterns of activity. These systems flag any claims that look suspicious due to their aggregate scores or relation to threshold.
These systems flag any claims that look suspicious due to their aggregate scores or relation to threshold.To work, investigators must be able to update and modify the rules whenever they come across new types of fraud rather than waiting for a manual every year,
Statistical analysis takes the guesswork out of threshold setting by empirically determining “normal” ranges for predetermined metrics. Key performance indicators (KPIs) associated with tasks or events are base-lined and thresholds set. When a threshold for a particular measure is exceeded, then the event is reported for further investigation.
This method of fraud scheme discovery uses data mining tools and builds programs that produce fraud-propensity scores. Claims are automaticallyscored for their likelihood to be fraudulent and made available for review.
Large volumes of seemingly unrelated claims can be checked, and then patterns and problems identified. The extent of connections between certain types of entities may be found to be much greater than would normally be expected, based on statistical analysis of other “networks” of entities.Example 1) social network analysis might show multiple durable medical equipment providers that are owned by several individuals withsimilar names and share a large percentage of similar patients. 2) Multiple claims in a short period of time from related parties, e.g. family members or ‘doctor shopping’