title: A new framework for describing and quantifying the gap between proof and practice.
abstract: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of General Internal Medicine, Department of Medicine, and Program in Biological and Medical Informatics, University of California San Francisco, San Francisco, California 94143-0320, USA. sim@medicine.ucsf.edu
authors.forename: Ida
authors.initials: I
authors.lastname: Sim
authors.forename: Steven R
authors.initials: SR
authors.lastname: Cummings
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use*; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine; D006300:Health Services Misuse; D006801:Humans; D008962:Models, Theoretical; D009203:Myocardial Infarction / Q000188:drug therapy*; D010351:Patient Discharge; D010818:Practice Patterns, Physicians' / Q000592:standards / Q000706:statistics & numerical data*; D014481:United States
chemical_list: D000319:Adrenergic beta-Antagonists