Addressing Revenue Leakage

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Micah Litow, Kalderos’ president and COO, discusses the importance of identifying and resolving noncompliant discount requests, along with addressing the gross-to-net gap for drug manufacturers.

Micah Litow

Micah Litow

Kalderos, a data infrastructure and analytics company, collaborates with drug manufacturers, government agencies, healthcare providers, and payers to tackle one issue at the top of the docket: drug discount management. Pharma Commerce spoke with Micah Litow to discuss the contributing factors surrounding revenue leakage, and how manufacturers are handling fragmented data.

*This interview has been edited for length and clarity.

Pharma Commerce: Revenue leakage can be a reality of doing business for drug manufacturers. What are some of the main contributing factors?

Litow: Multiple factors contribute to revenue leakage for drug manufacturers. The most obvious—and the one we focus on—is noncompliant drug discounts, particularly duplicate discounts submitted under the 340B Drug Pricing Program and the Medicaid Drug Rebate Program. Manufacturers have a statutory protection from duplicate discounts occurring on the same utilization from both of these programs.

Noncompliant discounts also occur in the commercial managed care space with rebates related to privately insured patients and Medicare patients. A commercial payer may request rebates from a manufacturer, even though not all rebate requests necessarily fit with what the parties have contractually agreed upon.

The gross-to-net (GTN) gap for drug manufacturers includes all price concessions, the vast majority of which are compliant on a percentage basis. In 2021, the GTN gap for manufacturers totaled $236 billion, up from $75 billion in 2012. More than half of this is made up of 340B discounts and Medicaid and commercial rebates. As these programs have grown and the overlap has become more substantial, the total volume of noncompliant or ineligible rebates has also grown, increasing revenue leakage. Meanwhile, as federal and state drug discount programs become increasingly complex—with each state maintaining and changing its own preferred drug list and the Inflation Reduction Act (IRA) introducing new discount programs, among other variables—revenue leakage totals will climb without better tools to manage these programs. Thus, drug manufacturers will be under even greater pressure to detect noncompliant rebates.

How are drug manufacturers currently handling the disparate and fragmented data they receive from state Medicaid programs? And why is that causing issues for tracking and reporting noncompliant discounts?

Typically, a manufacturer has an operations team that deals with this data. They’ll get a summary-level invoice, which doesn’t include claims-level data, from a state Medicaid program. If a manufacturer has any questions about the total owed in rebates, they can try to track down claims-level data and check it against the summary-level data to determine whether they think the summary-level data is accurate. If manufacturers are spending the time to reconcile these data sets, the only real tools a manufacturer has is a web of complex spreadsheets or attempts to use structured query language (SQL).

Drug manufacturers also can work with what are known as third-party scrubbers, service providers that can help look at these invoice requests and see if the summary-level amount feels accurate. But what they’re really doing is kind of applying a set of rules to the spreadsheet data based on historical averages and trends, and if something looks egregiously wrong, they can pull that out and dispute it. Overall, though, because they’re receiving a massive amount of data and have a 38-day payment period to either pay the state Medicaid program or formally dispute the invoice, drug manufacturers are highly focused on speed and efficiency. They are driven by fear that if they don’t pay within the 38 days, they’ll incur an interest penalty.

Many times, especially in under-resourced operations teams, manufacturers know a percentage of the summary-level invoice comprises noncompliant rebates, which drive revenue leakage. But they simply don’t have the resources to determine what part of the summary invoice is not compliant. People using spreadsheets can’t meet the demands of analyzing claims-level data for noncompliant discounts at scale. You really need a technology solution that enables a team to perform compliance checks consistently with high precision at scale.

Why does lack of transparency contribute to inefficiency and a lower gross-to-net margin for drug manufacturers?

A big part of where transparency comes in is how the overlapping drug discount programs have been operationalized, the data that accompanies (or does not) each rebate/discount/refund request, and the timing associated with when invoices show up and the associated payment terms. That is what drives a lot of the noncompliance overlap between 340B and Medicaid.

The 340B discount typically is effectuated via the chargeback mechanism that is managed via wholesalers. Meanwhile, the Medicaid rebate is typically processed using the IT infrastructure that is also leveraged for reimbursement and is typically processed by third-party agents for the states. These separate IT infrastructures produce disparate data sets, leaving drug manufacturers with little visibility regarding the exact discounts and rebates applied to a specific dispense. A covered entity (CE) tracks individual 340B dispenses (often identifying 340B eligibility after the fact) and replenishes an equivalent amount of drug at a 340B discounted price. The wholesaler, who purchased that drug at full price, charges back the difference to the manufacturer. And the manufacturer primarily sees this chargeback. These pricing adjustments occur on separate transaction rails. The lack of transparency for manufacturers around how the initial discounted purchase maps to individual dispenses, and then how those potentially overlap with Medicaid rebates being requested, drives a lot of the noncompliance.

This contributes to inefficiency, because when you lack transparency, so much of what you’re doing is trying to get these different data sets to line up with one another. And every stakeholder spends a lot of time trying to get the siloed data sets to align and make sense.

Since there’s not one central source of truth that everyone is drawing upon and relying on, it takes a lot of work to create visibility among all the different parties, even when they’re trying to connect with one another and create that visibility. Ultimately, that does result in a lower gross-to-net margin because that inefficiency, that lack of communication, drives noncompliance and contributes to revenue leakage.

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