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Related Experiment Videos

A new framework for describing and quantifying the gap between proof and practice.

Ida Sim1, Steven R Cummings

  • 1Division 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

Medical Care
|July 30, 2003
PubMed
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Quantifying proof-to-practice gaps using the Number Not Prevented (NNP) metric reveals significant underuse of interventions like beta-blockers post-myocardial infarction. This framework aids in identifying and addressing clinical practice gaps for better patient outcomes.

Area of Science:

  • Healthcare quality improvement
  • Clinical informatics
  • Health services research

Background:

  • Significant discrepancies persist between clinical research evidence and everyday medical practice.
  • A novel framework is introduced to define, analyze, and quantify these proof-to-practice gaps.

Purpose of the Study:

  • To present a systematic framework for quantifying proof-to-practice gaps.
  • To introduce the Number Not Prevented (NNP) metric for measuring intervention underuse and overuse.

Main Methods:

  • Uptake curves for interventions are plotted over time for candidates and non-candidates.
  • Underuse NNPs quantify preventable disease events missed due to candidate underuse.
  • Overuse NNPs quantify adverse events or missed opportunities in non-candidates.

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Main Results:

  • Application to beta-blocker underuse post-myocardial infarction in the US showed 2995 annual deaths not prevented.
  • The framework identified challenges in defining proof, efficacy, efficiency, and eligible patient populations.
  • Sensitivity analysis indicated a wide range for annual NNPs (455-20,409).

Conclusions:

  • NNP league tables can help policymakers compare clinical impacts of intervention underuse and overuse.
  • The NNP framework offers a systematic approach to analyzing proof-to-practice gaps.
  • Gap analyses can guide resource allocation to interventions with the greatest clinical consequences.