An earlier post — Exclusion List exposure: A problem you don’t know you have — highlighted the financial impact associated with healthcare providers and entities that have been deemed “bad actors.” It also shared what the IQVIA Gross-to-Net team learned through a recent data-driven analysis using public data sources and IQVIA Longitudinal Access and Adjudication Dataset (LAAD) pharmacy claims data. (If you haven’t already, you may want to read that post before you continue.)
For the second post in our series, we’re taking a closer look at the universe of Exclusion Lists and why they can be challenging to monitor. The Centers for Medicare and Medicaid Services (CMS), the Office of the Inspector General (OIG), and states all maintain lists to identify providers and entities that should be excluded from payments and hiring.
What follows is a brief overview of the major lists that every pharmaceutical company needs to proactively monitor:
Given the number of “bad actor” lists, variations in data formatting, and the lack of NPI numbers, monitoring Exclusion and Preclusion Lists is no small task. Yet, related risks — to compliance, program integrity, and contracting effectiveness — are huge. The size and scope of the challenge calls for an automated, data-driven approach, which is what the IQVIA Gross-to-Net team recently explored through our analysis.
Using publicly available data from OIG, SAM, states, and CMS, IQVIA identified about 17,000 unique IDs (mostly NPIs). By overlaying LAAD pharmacy claims data, we linked 3,000 HCP NPIs and 26 pharmacy NPIs to 2.6 million pharmacy claims where the fill data occurred during the period of exclusion.
To identify more excluded and precluded prescribers, we flagged rejection codes in the LAAD claims data. Two rejection codes — A1 and 9292 — indicate membership on “bad actor” lists. An A1 rejection is associated with an Excluded Provider (per OIG’s LEIE, SAM, and state Medicaid lists), while a 929 rejection indicates a Precluded Prescriber.
We found about 9,000 HCPs to whom these codes had been applied. From there, we identified around 4,000 NPIs — and 7.5 million claims — with at least one A1 or 929 rejection during the month corresponding to the fill date.
With thousands of affected providers and millions of affected claims, our analysis illuminates the scope and scale of the risk. As noted in the first blog, we estimate the impact on 2021-2022 drug expenditures to be $893 million (of which, 51% was contributed from Commercial payers, 33% from Medicare Part D, and 11% from Medicaid). The Top 20 drugs contributed about $271 million — or 30% — of the total impact.
How many rebates are being paid based on prescribers written by “bad actors”? How much are those rebates costing your company — and what are the compliance risks you’re bearing as a result? To understand the impact of Exclusion and Preclusion List exposure, rebate claims must be populated with NPIs. Yet most of the time, that field is missing. IQVIA has a differentiated process for assigning NPIs to a large percentage of rebate claims and applying rejection codes in IQVIA LAAD pharmacy claims data to the public lists.
Transparency remains a very large hurdle for the industry, and connecting the critical dots will be a requirement for any company to understand their risk and exposure. We’ll take a closer look at how to do that in the final blog of this series.
1 e.g., Medicaid Exclusions | Office of the Medicaid Inspector General (ny.gov)
2 https://www.ncpdp.org/NCPDP/media/pdf/VersionD-Questions.pdf, pp. 67-68
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