Health care fraud is an epidemic in the US, affecting the nation’s wellness as well as its health care costs. Fraud analytics tools help to detect and eliminate this dangerous and expensive crime.
How Much Does Health Care Fraud Cost?
The National Heath Care Anti-Fraud Association estimates conservatively that health care fraud costs the nation about $68 billion annually. And the repercussions from misdiagnosed illnesses, inadequate treatment, unnecessary procedures and dangerous practices that result from this fraud can’t be quantified.
While fraud investigators and the law enforcement community work towards finding and prosecuting health care fraud, the volume of data produced by the health industry keeps multiplying. This is where fraud analytics comes in.
It’s impossible to pick through that much information to find suspicious transactions, or even look for anomalous data the way you could in, for example, a company’s accounts payable records.
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“Many analysts are focused on specific, known providers, or codes… well there are roughly 900,000 active physicians, 8,000 CPT, and 68,000 ICD-10 codes” says Jim McCall, Director of Fraud Analytics at Change Healthcare.
The key with fraud analytics is to stop telling the data what to look at, and let the data tell you what to look at, he says.
“Effective forensic analytics will interrogate the data to present you with questions that require answers, and help you see the behaviours that you otherwise wouldn’t see.”
4 Components of Fraud Analytics
It’s easy to say you need to look at the data, but given the huge volume of claims data, it can be overwhelming to figure out where to start.
“It’s not like looking for a needle in a haystack, it’s like looking for a needle in a farm full of haystacks,” says McCall.
Therefore, he suggests most healthcare fraud schemes will manifest in one or more of the following behaviours.
Is there a behaviour that is occurring at an atypical rate of occurrence?
Such behaviour may be the result of billing for services not rendered, or rendered unnecessarily.
Look at the frequency of procedures, diagnoses, modifiers and patients.
Examine concentration within a visit or specific time frame.
Common examples include an unusual volume of services within a specific visit, an abnormally “thick” percentage of patients with a specific diagnosis code, or the classic “impossible day”, with too many patients seen on a particular date of service.
Relates to the “weight” of a service.
Services that have varied levels of complexity, and, in turn, reimbursement, lend themselves to “upcoding”, which is billing for a higher level of service than was provided in order to achieve an unentitled reimbursement.
Temporal analysis of spikes, or surges.
Spikes are sudden and sharp increase or decrease, and surges, where a surge is a longer sustained trends.
Think of it like a big wave versus a rising tide. In healthcare fraud, you can see this in metrics such as patient visits, procedures, amounts paid.
Behavior Classification Model for Fraud Analytics
McCall has devised the Healthcare Fraud Suspect Behaviour Classification Model as a system of analytics that looks at these four elements to identify the highest risk.
“The four components have an inherent nexus. Many schemes include more than one of four classifications. Examining them together adds precession to your suspicion index” he says.
“Once you find an anomaly in one, or more of those four classifications, it will guide you to efficiently focus on the specific details you need to dive into.”
A Targeted Method for Examining “Big Data” for Fraud
This method helps to uncover suspicious behaviour without being overwhelmed.
In order to extend its portability, the model was designed to be powerful, yet simple to understand.
In fact, although enhanced with powerful fraud analytic tools, with some limitation and modification, any user with access to Excel can benefit from the model’s application.
But Don’t Stop There
However, there are many other internal and external data sources that can materially enhance the effectiveness of your models,” says McCall.
Using Many Sources of Data
For example, there is a treasure trove of public information to be mined to provide additional context and insight to help sharpen the point on analytics.
These additional data sources may help explain away what appeared to be ‘bad’ billing or, conversely, deepen the level of concern.
As a simple analogy, this is similar to how travellers do some research before selecting accommodations or restaurants.
They look at sites like Trip Advisor, for aggregation of multiple inputs, says McCall.
Think Outside the Box
Perform several different analytic techniques on the data to identify areas of concern, says McCall.
“Surely the content may not specifically scream ‘fraud’, although sometimes it does, such as prior convictions. More often, it may provide evidence of financial stress, relationships to known offenders, and/or high risk billing locations. If you can combine that context with the anomalies from a claims perspective, you just impacted the level of suspicion, either though addition or subtraction.”
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There is no shortage of external data that can be helpful.
An additional example includes information on the specific location where the services are purportedly being rendered.
“Is the address a legitimate business? Who owns the location?,” he says, noting that there is always another dimension to the complexity of potential fraud such as organized criminal enterprises that have been known to rent space in legitimate medical office parks to mask the suspicious behaviour of their activities.
So you might have data anomalies flagged in your claim analytics and data from other sources that you’ve gathered, but to find that right haystack, in the farm of haystacks, and then find the needle in that specific haystack, says McCall: “You have to put all those pieces together.”