Dr. Fraudster & the Billing Anomaly Continuum

healthcare-fraudThis month’s member’s lecture on Medicare and Medicaid Fraud triggered a couple of Chapter member requests for more specifics about how health care fraud detection analytics work in actual practice.

It’s a truism within the specialty of data analytics having to do with health care billing data that the harder you work on the front end, the more successful you’ll be in materializing information that will generate productive results on the back end.  Indeed, in the output of health care analytics applications, fraud examiners and health care auditors now have a new set of increasingly powerful tools to use in the audit and investigation of all types of fraud generally and of health care fraud specifically; I’m referring, of course, to analytically supported analysis of what’s called the billing anomaly continuum.

The use of the anomaly continuum in the general investigative process starts with the initial process of detection, proceeds to investigation and mitigation and then (depending on the severity of the case) can lead to the follow-on phases of prevention, response and recovery.   We’ll only discuss the first three phases here as most relevant for the fraud examination process and leave the prevention, response and recovery phases for a later post.

Detection is the discovery of clues within the data.  The process involves taking individual data segments related to the whole health care process (from the initial provision of care by the health care provider all the way to the billing and payment for that care by the insurance provider) and blending them into one data source for seamless analysis.  Any anomalies in the data can then be noted.  The output is then evaluated for either response or for follow-up investigation.  It is these identified anomalies that will go on at the end of the present investigative process to feed the detection database for future analysis.

As an example of an actual Medicare case, let’s say we have a health care provider whom we’ll call Dr. Fraudster, some of whose billing data reveals a higher than average percentage of complicated (and costly) patient visits. It also seems that Dr. Fraudster apparently generated some of this billings while travelling outside the country.  There were also referred patient visits to chiropractors, acupuncturists, massage therapists, nutritionists and personal trainers at a local gym whose services were also billed under Dr. Fraudster’s tax ID number as well as under standard MD Current Procedural Terminology (CPT) visit codes.  In addition, a Dr. Outlander, a staff physician, and an unlicensed doctor, was on Dr. Fraudster’s staff and billed for $5 an hour.  Besides Outlander, a Dr. Absent was noted as billing out of Dr. Fraudster’s clinic even though he was no longer associated with the clinic.

First off, in the initial detection phase, its seems Dr. Fraudster’s high-volume activity flagged an edit function that tracks an above-average practice growth rate without the addition of new staff on the claim form.  Another anomalous activity picked up was the appearance of wellness services presented as illness based services.  Also the billed provision of services while travelling is also certainly anomalous.

The following investigation phase involves ascertaining whether various activities or statements are true.  In Dr. Fraudster’s case, evidence to collect regarding his on-staff associate, Dr. Outlander, may include confirmation of license status, if any; educational training, clinic marketing materials and payroll records.  The high percentage of complicated visits and the foreign travel issues need to be broken down and each activity analyzed separately in full detail.  If Dr. Fraudster truly has a high complication patient population, most likely these patients would be receiving some type of prescription regime.  The lack of a diagnosis requirement with associated prescriptions in this case limited the scope of the real-life investigation.  Was Dr. Fraudster prescribing medications with no basis?  If he uses an unlicensed Doctor on his staff, presents wellness services as illness related services, and sees himself (perhaps) as a caring doctor getting reluctant insurance companies to pay for alternative health treatments, what other alternative treatment might he be providing with prescribed medications?  Also, Dr. Fraudster had to know that the bills submitted during his foreign travels were false.  Statistical analysis in addition to clinical analysis of the medical records by actual provider and travel records would provide a strong argument that the doctor had intent to misrepresent his claims.

The mitigation phase typically builds on issues noted within the detection and investigation phases.  Mitigation is the process of reducing or making a certain set of circumstances less severe.  In the case of Dr. Fraudster, mitigation occurred in the form of prosecution.  Dr. Fraudster was convicted of false claims and removed from the Medicare network as a licensed physician, thereby preventing further harm and loss.  Other applicable issues that came forward at trial were evidence of substandard care and medical unbelievability patterns (CPE codes billed that made no sense except to inflate the billing).  What made this case even more complicated was tracking down Dr. Fraudster’s assets.  Ultimately, the real-life Dr. Fraudster did receive a criminal conviction; civil lawsuits were initiated, and he ultimately lost his license.

From an analytics point of view, mitigation does not stop at the point of conviction of the perpetrator.  The findings regarding all individual anomalies identified in the case should be followed up with adjustment of the insurance company’s administrative adjudication and edit procedures (Medicare was the third party claims payer in this case).  What this means is that feedback from every fraud case should be fed back into the analytics system.  Incorporating the patterns of Dr. Fraudster’s fraud into the Medicare Fraud Prevention Model will help to prevent or minimize future similar occurrences, help find currently on-going similar schemes elsewhere with other providers and reduce the time it takes to discover these other schemes.  A complete mitigation process also feeds detection by reducing the amount of investigative time required to make the existence of a fraud known.

As practicing fraud examiners, we are provided by the ACFE with an examination methodology quite powerful in its ability to extend and support all three phases of the health care fraud anomaly identification process presented above.  There are essentially three tools available to the fraud examiner in every health care fraud examination, all of which can significantly extend the value of the overall analytics based health care fraud investigative process.  The first is interviewing – the process of obtaining relevant information about the matter from those with knowledge of it.  The second is supporting documents – the examiner is skilled at examining financial statements, books and records.   The examiner also knows the legal ramifications of the evidence and how to maintain the chain of custody over documents.  The third is observation – the examiner is often placed in a position where s/he can observe behavior, search for displays of wealth and, in some instances, even observe specific offenses.

Dovetailing the work of the fraud examiner with that of the healthcare analytics team is a win for both parties to any healthcare fraud investigation and represents a considerable strengthening of the entire long term healthcare fraud mitigation process.

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