Advancing the field of pharmaceutical risk minimization through application of implementation science best practices.

Regulators are increasingly mandating the use of pharmaceutical risk-minimization programs for a variety of medicinal products. To date, however, evaluations of these programs have shown mixed results and relatively little attention has been directed at diagnosing the specific factors contributing to program success or lack thereof. Given the growing use of these programs in many different patient populations, it is imperative to understand how best to design, deliver, disseminate, and assess them. In this paper, we argue that current approaches to designing, implementing, and evaluating risk-minimization programs could be improved by applying evidence- and theory-based 'best practices' from implementation science. We highlight commonly encountered challenges and gaps in the design, implementation, and evaluation of pharmaceutical risk-minimization initiatives and propose three key recommendations to address these issues: (1) risk-minimization program design should utilize models and frameworks that guide what should be done to produce successful outcomes and what questions should be addressed to evaluate program success; (2) intervention activities and tools should be theoretically grounded and evidence based; and (3) evaluation plans should incorporate a mixed-methods approach, pragmatic trial designs, and a range of outcomes. Regulators, practitioners, policy makers, and researchers are encouraged to apply these best practices in order to improve the public health impact of this important regulatory tool.