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Background
Payers frequently rely on budget impact model (BIM) results to help determine drug coverage policy and its effect on their bottom line. It is unclear whether BIMs typically overestimate or underestimate real-world budget impact.Objective
We examined how different modeling assumptions influenced the results of 6 BIMs from the Institute for Clinical and Economic Review (ICER).Study Design
Retrospective analysis of pharmaceutical sales data.Methods
From ICER reports issued before 2016, we collected estimates of 3 BIM outputs: aggregate therapy cost (ie, cost to treat the patient population with a particular therapy), therapy uptake, and price. We compared these against real-world estimates that we generated using drug sales data. We considered 2 classes of BIM estimates: those forecasting future uptake of new agents, which assumed “unmanaged uptake,” and those describing the contemporaneous market state (ie, estimates of current, managed uptake and budget impact for compounds already on the market).Results
Differences between ICER's estimates and our own were largest for forecasted studies. Here, ICER's uptake estimates exceeded real-world estimates by factors ranging from 7.4 (sacubitril/valsartan) to 54 (hepatitis C treatments). The “unmanaged uptake” assumption (removed from ICER's approach in 2017) yields large deviations between BIM estimates and real-world consumption. Nevertheless, in some cases, ICER's BIMs that relied on current market estimates also deviated substantially from real-world sales data.Conclusions
This study highlights challenges with forecasting budget impact. In particular, assumptions about uptake and data source selection can greatly influence the accuracy of results. 相似文献A key goal for implementation science is the identification of evidence-based consultation protocols and the active ingredients within these protocols that drive clinician behavior change. The current study examined clinicians’ self-coding of fidelity as a potential active ingredient of consultation for the Attachment and Biobehavioral Catch-up (ABC) intervention. It also examined two other potential predictors of clinician fidelity in response to consultation: dosage of consultation and working alliance. Twenty-nine clinicians (97% female, 62% White, M age?=?34 years) participated in a year of weekly fidelity-focused ABC consultation sessions, for which clinicians self-coded fidelity and received consultant feedback on both their coding and their fidelity. Data from the ABC fidelity measure were available for 1067 sessions coded by consultants, and clinicians’ self-coding accuracy was calculated from 1044 sessions coded by both clinicians and consultants. Alliance was measured with the Working Alliance Inventory—Trainee and Supervisor Versions. The study was observational, and fidelity and self-coding accuracy were modeled across time using hierarchical linear modeling. Clinicians’ ABC fidelity, as well as their self-coding accuracy, increased over the course of consultation. Clinicians’ self-coding accuracy predicted their initial fidelity and growth in fidelity. Working alliance was also linked to fidelity and self-coding accuracy. These results suggest that clinician self-coding should be further examined as an active ingredient of consultation. The study has important implications for the design of consultation procedures and fidelity assessments.
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