The 340B program has been on the minds of many pharmaceutical, healthcare, public policy, and legal stakeholders of late. Regardless of how the so-called “mega-rule” evolves, most drug manufacturers cannot completely monitor the use and potential abuse of inventories fully through covered entities and contract pharmacies to patients. This is particularly frustrating to the drug manufacturing and payer community co-funding the program, both of which want to ensure its ongoing success but are legitimately alarmed at the excessive volumes cannibalizing commercial lines of business within the hospitals and larger health systems comprising more than 80% of the total 340B drug purchases.

While controlling program abuse and duplicative discounting is the ultimate goal, most manufacturers we work with simply want transparency and visibility into rudimentary business facts they are unable to see today as a first step:

  • Who are the top covered entities and what are their usage trends?
  • Which covered entities own their own pharmacies, contract for 340B services, or use a combination of both?
  • How can I see a top-down view of all purchasing trends broken down by the correct class of trade?
  • How much of my product is being purchased for 340B vs. commercial vs. managed Medicaid patients?
  • How many of my products are being sold through retail pharmacies (i.e., contract pharmacies) under the 340B program vs. other segments?
  • Are outpatient inventories supplementing inpatient patient pharmacies?
  • What portion of my payer rebates is for products sold through a contract pharmacy at the 340B price?
  • How much is it worth to my organization if we were to take action vs. the risks and customer ill-will?

In today’s age of big data and advanced analytics, these questions should be relatively easy to address, but they are not. In a nutshell, most companies do not have the expertise or resources to manage the data, rationalize diverse datasets and fill in data gaps. Why?

  • Data Volumes – at last check the HRSA database contained approximately 29,000 covered entities and 59,000 contract pharmacies. Each quarter there are hundreds or thousands of changes with which to keep up.
  • Data Rationalization–indirect sales data such as 867 and chargeback data do not consistently identify 340B sales to contract pharmacies for which the product is shipped to the retail location but is billed to the covered entity. Consequently, sales to the pharmacy need to broken down into commercial vs. 340B class of trade, and similarly, sales at the covered entity level need to include sales from pharmacies outside of its legal entity hierarchy. Attempting to link rebate data also creates a host of challenges as well.
  • Inconsistent Identifiers – wholesalers, payers, and manufacturers all might be using account identifiers differently (340B ID, HIN, DEA, NPI, NABP, etc.) for sales, chargeback, and rebate data. Cross-walking 340B identifiers to HIN and DEA numbers is a massive effort for each manufacturer to make.

Starting in early 2014, IntegriChain adapted its sophisticated Master Data Management platform and assigned dedicated resources to address the data challenges outlined above. Additionally we have built a series of analytic dashboards that we plan to launch in July. Clearly there is much more work to be done, and we are seeking more active industry feedback.

If 340B program monitoring is an initiative for your organization, please contact me at or your IntegriChain representative for more detailed information.

Watch for release notes and blogs about IntegriChain’s 340B capabilities in the coming months on our advances in 340B analytics.

About the Author

Dave Weiss

Dave Weiss

Vice President, Industry Solutions

David Weiss, a software industry veteran, is charged with leading IntegriChain’s effort to provide pre-sales business consulting to life sciences manufacturers in the areas of needs assessment, analytics design, and value engineering. Prior to IntegriChain, David led the solutions and product marketing organizations at Model N, SAP, and IDS as well as spent five years as a management consultant for PWC, KPMG, and Knowledgent.