Medical fraud and its implications Dr Vaikuthan Rajaratnam

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Cost, Implications and strategies to deter, detect and prevent medical fraud

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  • 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’
  • Medical fraud and its implications Dr Vaikuthan Rajaratnam

    1. 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. 2. Healthcare expenditure lost to fraud annually Global estimate US$415 billion (~1.3 trillion MYR) Europe 56 billion euros (~240 billion MYR) Source: European Healthcare Fraud & Corruption Network
    3. 3. Annual cost of medical fraud Proportion of healthcare expenditure lost to fraud or error not known Estimate: At least 3%, probably more than 7% and possibly as much as 10% Source: The Financial Cost of Healthcare Fraud 2011 Report, PKF (UK) LLP and University of Portsmouth
    4. 4. Medical Fraud Estimate Malaysia • Health care is 4.75 % of 303.5B = 14.4B • Medical fraud estimated at 3% - 10% • US$ 0.4b to 1.4b or
    5. 5. Implications of medical fraud Insurance premiums Medical charges Taxes Health risks
    6. 6. 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
    7. 7. What is medical fraud? Fraud, waste or abuse of healthcare resources/funds, regardless of whether intent is proven (includes errors).
    8. 8. Why?
    9. 9. 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
    10. 10. 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
    11. 11. Examples of professional medical fraud Billings Procedures Quality Prescriptions Devices and Implants were never provided
    12. 12. 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
    13. 13. 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
    14. 14. Attorney General Eric Holder, who announced the $2.2 billion settlement with Johnson & Johnson Monday, Nov. 4 in Washington, D.C. / CBSNEWS
    15. 15. 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
    16. 16. Fraud Risk Management Cycle CIMA UK
    17. 17. Anti Fraud Strategy
    18. 18. 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.
    19. 19. 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
    20. 20. 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
    21. 21. 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
    22. 22. 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
    23. 23. 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.
    24. 24. 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.
    25. 25. selected target areas • • • • • High volume of services High cost Dramatic change in frequency of use High risk problem-prone areas Recovery Auditor
    26. 26. • minimize potential future losses to finaciers through targeted claims review while using resources efficiently and treating providers and beneficiaries fairly
    27. 27. Restoring integrity in the medical profession Professionalism Accountability Legislation Probity
    28. 28. 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)

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