Data Mining for Payment Integrity–knowing which rocks to turn over

MedReview works with clients to help them uncover savings opportunities. Inevitably, there are errors–and dollars–that have been overlooked when a claim is paid. With MedReview’s data-mining solution, our experts examine many different claim sets and recoup funds that otherwise are overpaid in error.

Several payment integrity companies offer data mining, but MedReview prides itself on data-mining success based on

• A team of experts who average a decade or more of claims experience
• An approach that closely examines the contractual specifics between payor and provider/member
• A fully collaborative relationship that augments and bolsters in-house data-mining teams and shares knowledge of how to prevent future claims leakage
• Meaningful client relationships with assistance that extends from pre- to post-implementation

At MedReview, “We’re willing to work with the team and the company to make sure we’re in alignment with their goals and figuring out the best way we can find the most overpayments for them as quickly as possible,” says Michael Brown, Senior Director of Data Mining.

Data mining: what is it?

What is data mining, and why do it? Explains Brown, “We’re looking for any overpayment in post-payment claims–it could be the fault of the plan, it could be a billing error, it could be a multitude of different factors. We know there’s claim leakage and overpayments whenever claims go through a final batch process and pay. That’s the large overview of what data mining is.”

A core tenet of data mining is examining contractual relationships that payors have with outside organizations and ensuring that payors are being billed according to those contractual terms.

“We are truly looking for anything that was overpaid based on your business rules,” says Brown.

MedReview doesn’t stop there, however. We determine the categories in which an overpayment is most likely to occur and develop tools to efficiently review those categories and identify overpayments–applied algorithms we call “concepts.”

The MedReview team has the “ability not just to go in and find overpayments, but to develop a concept” that examines “the ways in which a claim could be overpaid based on different criteria,” says MedReview Chief Strategy Officer Kathy Gonzales. MedReview’s data-mining experts draw on their extensive claims knowledge to target where claims leakage is most likely to occur. “They’re constantly creating these concepts, to find new ways to go in and find more overpayments,” says Gonzales.

Without proper data mining, payors can expect a 1-2% loss on claims due to overpayment. Through applying concepts, MedReview’s data-mining team can locate claim overpayments and return funds to payors. And the more concepts are applied, the more savings add up.

MedReview currently uses more than 500 concepts, tailoring them to the specific client, as well as concepts developed for specific clients. The secret to efficiently executing these concepts, says Brown, is “knowing where to look and going there first.”

Concept Examples

The most common concepts in data mining address:

• Membership issues/retro-termination
• Duplicates, i.e., a claim is paid more than once
• Pricing issues, such as a retro rate load, data entry error, or incorrect authorization
• CMS secondaries and commercial COB, which can be overlooked or incorrectly identified, resulting in overpayment
• Non-covered services paid in error, such as when a limit or maximum has been reached
• Excessive units, e.g., a provider is supposed to bill once per week, and they bill seven days
• Billing errors, such as from billing contractors unaware of policy specifics
• Incidentals/bundling, or failing to comply with NCCI or state edits specifying the bundling of certain items
• Other primary insurance that has been missed or overlooked and should be billed
• Treatment/frequency, e.g., billing a patient’s payor twice for an appendectomy

Expertise:

Not only are MedReview’s data-mining experts able to draw from extensive experience in claims management, but they also possess specialized knowledge that a payor’s in-house team may lack.

“Plans do use some level of data mining within their organizations at some primary level,” acknowledges Gonzales, “but our focus is bringing in experts who are spending 100% of their time on data mining,” who are capable of accomplishing tasks payors may not be able to devote sufficient time to.

MedReview’s data-mining operations experts have been in healthcare claims processing for more than a decade, “if not two decades or more,” says Brown, which is a huge benefit for payors. “I’d value being on the payor side of operations knowing that I’m talking to someone who can feel my pain versus just selling me something, who doesn’t know what I’m talking about if I dig in even peripherally,” he adds.

“We’re healthcare experts looking to understand your business,” Brown says. Payors can hire someone at a lower rate for basic data mining, but basic results is all they will get. Brown describes MedReview as the place to go “if you want someone who’s going to understand your business, look at the specific contract rules for exceptions, and dig in.”

Brown compares the potentially arduous process of finding claim leakage to turning over dozens of rocks. The advantage MedReview’s team brings, says Brown, is that “we know which rocks to look under,” which equals faster, more successful results.

Working with MedReview’s data-mining managers, payors are able to find claim leakage more rapidly. In-house staff might have a backlog of claims to review, and it might take them six months to get to a claim and find an error that MedReview, diving in right away, can find in one month. If the leakage is due to a system-wide error, payors can address the issue and upgrade their system five months sooner, saving time as well as money and effectively stopping up a source of ongoing leakage.

Solid client relationships

MedReview’s attentive customer service and detailed claims exploration are exemplified in our relationship with P3 Health Partners, one of our most recent data-mining clients. MedReview helped P3 identify post-pay claim overpayments and continues to cultivate the relationship.
Timothy Wilson, senior vice president of P3, said he chose MedReview for their expertise, to complement their in-house team and bring new resources to P3’s data-mining efforts. MedReview “also has access to tools and technology that we just don’t have access to,” says Wilson. “Even on a post-pay basis,” Wilson says, MedReview’s “results are fast; they are accurate. And they really help us understand where to look for savings.”

Brown understands the value of nurturing such client relationships. “To me, it’s how you’re treating the client–you’re giving them good quality and service, and they don’t feel that you’re abandoning them because you got a better offer. You’re dancing with the one you came with.” In addition, “We’re hands-on in the implementation,” says Brown. MedReview meets with its data-mining clients, “at least weekly, if not more frequently.”

First and foremost, it’s a collaboration

Brown reassures in-house payment integrity and data-mining teams, “We’re here to help you, to augment or give more savings to your group and make you look good. We’re not there to replace anyone; we’re there to assist.” When a claim is processed incorrectly or overpaid, a data mining vendor such as MedReview is “a second set of eyes” to capture overpayments and return them to the payor, “to supplement or augment savings that you are finding.”

As in other industries, healthcare plans are being asked to do more with fewer resources. “If you don’t have the resource of time, or the ability to train staff, [MedReview’s] data mining is a great option to capture leakage,” says Brown. “We find things that you’re paying wrong today, and that can help you for tomorrow. You can then take the information that we’re giving you and revisit it,” making updates accordingly. Payors could “have a backlog of claims, and timely filing and pay laws forcing them to get some inventory done,” says Brown, in which case “data mining could be the backend” and recapture lost revenue.

MedReview’s data-mining solution allows payors to focus on their core competencies while we bring our core competencies to the table, get to know a client’s business inside and out, and develop concepts to maximize client savings. “We like to grow with our partners,” says Brown. “So we may start off small, but as we understand their business, where their pain points are, we grow together,” which can result in “an amplification of dollars over time and growth in our teams working together. But,” he emphasizes, “it’s collaborative.”

Find out how MedReview’s more than 500 data-mining concepts can help your organization build a dam to stop claim leakage. Contact us to learn more today.