As special agent supervisor with a U.S. state agency, I (Paul Kolb, CFE) was assigned a case involving allegations of fraudulent practices perpetrated by a home-health agency that billed Medicaid and other government-funded health care programs. Tobey Culler, a fraud analyst with the same state agency, and I used the U.S. Department of Health & Human Services (HHS) Office of Inspector General's (OIG) free RAT-STATS program to avoid pouring limited resources into a full-scale audit and file review.
The program gave us a random list of patient files to review based on a tested and proven statistical model. Our investigation led to the successful criminal conviction of the agency's owner while using a fraction of the resources of a typical, full-scale fraud examination.
Many cases, limited resources
With large caseloads and limited resources, fraud examiners need to be able to effectively and efficiently complete fraud examinations. A case is easier, of course, when we've isolated the fraudsters, discovered what they're doing and how they're committing the crimes. But even then, proving and quantifying fraud can be challenging. For example, when you receive an allegation that someone is committing fraud, is it appropriate to obtain and review every single file and transaction with which the suspect came in contact? Sometimes, but often your efforts might not yield good returns on your investment of resources.
Our state's health care fraud section received allegations involving Home Care Inc. At the time of the investigation, Home Care was billing the state Medicaid program about $100,000 a month — totaling more than $10 million since its start of business.
We've seen investigators spend hefty resources on examining big home-health agency case frauds, so we decided to take a different approach — statistical sampling.
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