For the New York State Office of the Medicaid Inspector General (OMIG), a “target” is not a department store or a bull’s-eye for archers. Rather, it’s a way of helping to prevent and detect fraudulent, abusive and wasteful practices within the state’s $52 billion Medicaid program.

The OMIG is committed to using its staff resources most efficiently, and so-called “targeting” is one way to do just that. What is targeting, and how does it work at OMIG? Staff uses sophisticated data mining tools to identify areas on which to focus from its vast universe of claims that would potentially yield the greatest
return on their time and effort, whether those are opportunities for pre-payment reviews and edits, system matches, audits or investigations.

Up until recently, the majority of targeting had been based on high dollar or claim amounts and surges in activity. Now, however, OMIG is using a powerful software solution developed by Salient Management Company to incorporate different types of investigative methods and information into the targeting process, such as relationships between providers, pharmacies and recipients; comparisons of diagnoses and drug use; and geographical considerations. It’s a simple premise: better targeting should produce better results.

OMIG’s Pre-Payment Review Unit is charged with reviewing claims before they are paid, which generates savings via cost avoidance versus reimbursement. “But we can’t possibly review every claim before it’s paid, so we try to target providers for specific behaviors,” explains Kathy Whitsett, a Data Analyst in the PPR unit. “We are working with limited resources and need to be as productive as possible.”

(Editor’s Note: “Upcoding” is a fraudulent practice in which provider services are
billed under higher procedure codes than were actually performed, resulting in a higher payment by Medicaid and Medicare.)

So when the Salient trainers addressed “upcoding” during one of their sessions, Whitsett was all ears. She immediately saw how she could use Salient’s investigative methods to expand the focus to her targeting efforts. She quickly turned the lessons learned in the classroom into a new upcoding project that could save the Medicaid program as much as $425,000 and create a sentinel effect that may lead to a cost avoidance of millions of dollars more.

As Whitsett describes in her project plan, in January 2009, Medicaid physician fees were increased, including reimbursements for the five codes (99211-99215) for “Evaluation and Management (E&M) of established patients in an office setting.” Reimbursements for these codes now vary according to the level of complexity of the visit, with the billable code determined by the amount of time spent obtaining patient history and performing the examination as well as the complexity of medical decisionmaking. Highly complex encounters are billed with higher level codes and are reimbursed higher amounts than less complex encounters. Interestingly, prior to 2009, physicians were reimbursed a flat rate regardless of the complexity of an office visit – in other words, a claim coded 99211 was billed the same amount as a claim coded 99215. With the January ’09 change in reimbursement methodology, however, physicians now receive a financial incentive to upcode E&M services in order to obtain higher reimbursements.

For the 12-month period ended February 2011, Whitsett found that physicians were reimbursed $58.2 million for 3.5 million 99211-99215 code claims, with an average reimbursement amount of $16.58. “Using Salient we immediately identified 2,352 providers who billed 99214 or 99215 exclusively over the same 12-month span, and were reimbursed $1,270,691 for 51,003 claims,” she explains. Of note, the average
reimbursement for this “collection” of providers was $24.91.

If the 2,352 providers had billed the 51,003 claims similar to their peers, Medicaid reimbursement for the 12 months would have been $ 845,630, accounting for a savings of approximately $425,000 (the difference between $1,270,691 and $845,630).

“Our objective is to educate targeted providers about their billing patterns in comparison to the average provider,” says Whitsett. “We are going to request information from them to support their historic billing patterns and observe and measure cost avoidance achieved as a result of changes in those patterns. We are also going to educate the providers about their responsibility to identify and reimburse overpayments under the Affordable Care Act. In doing so, we expect to achieve successful project outcomes as a result of data analysis and targeting with Salient.”

Salient users in the Pre-Payment Review Unit agree that they are much more productive with the tool than they were before. “It’s great for targeting because very quickly you can get a picture of what is going on by provider, service, recipient, cost,” according to Whitsett. “If I’m looking into an issue that can be potentially applied to a prepayment review, I have to first identify providers before I can go forward. I can do that now much more quickly with Salient than going through the data warehouse. Plus with Salient, I can look at data in different ways, whereas the data warehouse just gives me the data and I have to bring it into another program to manipulate it. Salient helps me start with a bigger focus and drill down to more specific targets. I use Salient as my starting point.”