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A proposed taxonomy for population-level prescription use patterns

02 January 2021
Volume 3 · Issue 1

Abstract

Recent and increasing discussion of prescription price transparency highlights the importance of defining, measuring and communicating prescription drug value. To help advance these goals, the authors propose a taxonomy of population-level prescription drug use patterns. The taxonomy assigns prescription use to one of five categories according to likely population-level health impact. The categories include effective, potentially discretionary, potentially harmful, wasteful, and lifestyle. The authors hope the proposed taxonomy will inform discussion of prescription drug value by providing estimates of population impact, especially the balance of anticipated benefit and harm.

Prescription drugs commonly evoke debates about value. Defining and measuring drug value, broadly or specifically, is complex. Recent and increasing discussion of drug price and price transparency in the US highlights the importance of measuring and communicating drug value (Califf and Slavitt, 2019). The need to explicitly measure value is most apparent for new, expensive drugs, but for every individual, each prescription use decision represents a trade-off between anticipated benefits, possible harms, cost and opportunity cost. To advance this discussion of value and inform debates about price, the authors propose a taxonomy of population-level drug use. With this taxonomy, broad categories related to the most probable balance of benefit and harm to support discussions about cost have been created. The authors propose that the taxonomy be employed for broad examination of drug use at the population level. When sufficient granular data are available, the taxonomy could be applied to individual-level clinical situations. The proposed taxonomy builds on past efforts to classify drug use patterns; the current expanded effort will provide rich context for ongoing discussions of drug use and ultimately value (Greenway and Ross, 2017; Lexchin, 2017).

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