Background. Substantial gaps often exist between every day practice and best practice as defined by research evidence. We present a framework for defining, analyzing, and quantifying such proof-to-practice gaps.
Method. An intervention's use can be plotted over time as ideal and actual uptake curves among candidates and noncandidates. Gaps of underuse are deviations from ideal uptake among candidates and can be quantified as underuse NNPs (Number Not Prevented): the number of disease events each year that would have been prevented, but were not, because of underuse among candidates of the intervention. Gaps of overuse are deviations from ideal uptake among non candidates and can be similarly quantified as overuse NNPs.
Results. Applying our method to the underuse of beta-blockers at hospital discharge postmyocardial infarction (MI) in the United States demonstrates an annual NNP of 2995 first-year post-MI deaths not prevented (sensitivity analysis range 455-20,409). Our NNP analysis framework highlights challenges to the determination of efficacy and efficiency, the definition of what constitutes proof, rapid recognition of proof when it does occur, the definition of eligible candidates, and the definition of the proportion of candidates treated.
Conclusion. League tables of NNPs can help policy makers compare the clinical consequences of underuse and overuse of diverse interventions, while the NNP framework provides a systematic approach for describing and analyzing the components of proof-to-practice gaps. Such gap analyses can help organizations direct their resources to reducing gaps of greatest clinical consequence.
Despite the billions of dollars that are spent on clinical research, published findings often have a dismayingly small impact on practice. For example, evidence from the 1980s showed that β-blockers reduce mortality in myocardial infarction (MI) survivors, 1,2 yet even in the mid-1990s, only 21 to 77% of eligible heart attack patients were receiving post-MI β-blockers. 3-5
How large are these proof-to-practice gaps? Where are the biggest gaps, and how do they compare across interventions? To answer these questions, a clear definition of proof-to-practice gaps is needed. In this paper, we formalize such a definition, present a framework for analyzing proof-to-practice gaps, and introduce a new measure that captures the clinical consequences of the underuse or overuse of proven interventions. Our approach can help organizations that are facing a multitude of proof-to-practice gaps to direct their resources to reducing gaps of greatest clinical consequence.