Modeling the Budgetary Impact of Payer Utilization Management Strategies: An Adapted Framework Based on Lessons...

Historically, budget impact models have been used to demonstrate how a new drug or technology entering the market could influence payer budgets, with the hope of influencing coverage of the drug or technology. This article proposes the adaptation of a budget impact model framework to answer the payer question "What is the budget impact of our approaches to managing the utilization of drugs and technologies?" Throughout this article, we also describe lessons learned while collaborating with a U.S. payer to evaluate the budgetary impact of specific antidiabetic drug utilization management approaches. DISCLOSURES: The study discussed in this commentary was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Lugo has nothing to disclose. Mullins reports grants and other fees from AHRQ, during the conduct of the study; grants and personal fees from Bayer and Pfizer; and personal fees from Boehringer-Ingelheim, Janssen/J&J, Regeneron, and Sanofi, outside the submitted work. Parts of the study were presented as a poster presentation at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. The study was also published as Hung's PhD dissertation.