Healthcare is a $2.7 trillion industry in the U.S. alone, and it is estimated that one-third is lost due to different forms of waste, mismanagement, and abuse. Waste includes such activities as providers prescribing unnecessary and redundant testing, devices and medications that are not better than the cheaper ones already in use, etc.
The common examples of fraud and abuse in healthcare include the following:
Illegal medical billing practices in which claims are falsified.
Multiple claims are filed by different providers for the same patient.
Patient identities are stolen and used to gain reimbursement for medical services never provided.
Collusion between unprincipled providers and their patients in which money from claims is shared.
It is estimated that 3%-10% of annual healthcare costs in the U.S. can be attributed specifically to fraudulent billing.
Further, a recent GAO report states that 68% of all medical fraud is the result of false billing and that healthcare providers are complicit in 62% of those cases, while patients are complicit in 14% of those.
The Solution – Healthcare Fraud Prevention With Big Data and Analytics
Clearly, the traditional healthcare fraud detection methods are not working. The more effective way to prevent fraud and abuse is to identify it before claims are paid. And that is why healthcare payers have now embraced the same predictive analytics that other sectors of the economy know to work.
Banks, for example, mine data that links consumer demographics and behaviors to predict the types of loan products that will be the most popular. Life, auto, and home insurance companies mine data to predict risk levels.
Now, it makes sense to approach fraud detection in healthcare using data mining techniques too.
How Predictive Analytics Works to Combat Fraud
Predictive analytics such as entity analytics identifies patterns that are potentially fraudulent and then develops sets of “rules” to “flag” certain claims. For example, a provider making a claim for a procedure that is outside of his/her area of expertise would be flagged for further scrutiny, because that is one of the “rules”.
But built into this healthcare fraud detection software model is AI, which will continually mine data, identify more and more emerging fraudulent patterns and create new “rules” for those as well. The “intelligence” in the system learns from these new rules and continually becomes more sophisticated in identifying, even more, fraud potentials. And the best models not only flag the potentials but provide the reasons for that flagging, so that investigations and assessments by management can be completed efficiently.
In short, a solid healthcare fraud auditing and detection system will provide protection to the payer in the following ways:
Identify inconsistencies and “rule-breaking” behaviors.
Detect and prevent potentially improper payments, by flagging them for review.
Continually mine data to identify new fraudulent patterns and develop new “rules” for those as well.
The beauty is in the big data that can all be mined and analyzed by one software tool, rather than a host of separate healthcare fraud detection systems that do not function in coordination, or worse, do not even “know” to check other Internet data sources. One of the most common types of fraud, for example, is the continued claims for an individual who has died. An antiquated system will not have this information, but a system that is “plugged into” big data will.