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Misunderstandings, misperceptions, and mistakes
  1. Sharon Straus, MD, MSc1,
  2. Brian Haynes, MD, PhD2,
  3. Paul Glasziou, MBBS, PhD3,
  4. Kay Dickersin, PhD4,
  5. Gordon Guyatt, MD, MSc5
  1. 1University of Calgary
 Calgary, Alberta, Canada
  2. 2McMaster University
 Hamilton, Ontario, Canada
  3. 3University of Oxford
 Oxford, UK
  4. 4Johns Hopkins University
 Baltimore, Maryland, USA
  5. 5McMaster University
 Hamilton, Ontario, Canada

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    Discussions about evidence-based medicine (EBM) have engendered both positive and negative reactions from clinicians, researchers, and policymakers since the term was first coined in the early 1990s.1,2 These discussions were brought to the forefront again in a recent commentary by Dr Bernadine Healy, former director of National Institutes of Health, in U.S. News & World Report.3 She raised several issues that EBM practitioners and teachers face when advocating this model of care. Firstly, she stated that EBM practitioners advocate using the “best” evidence which is mostly taken from randomised trials and cost benefit studies. Secondly, she raised the issues of the interpretation of evidence for screening mammography and prostate specific antigen as examples where EBM has failed because EBM proponents did not advocate for these tests based on the available evidence. Thirdly, she likened the practice of EBM to a “straitjacket” or a cookbook approach in which both clinician judgement and patient values and circumstances are ignored.

    All of these criticisms of EBM stem from misperceptions or misunderstandings and can be answered by careful consideration of the definition of EBM. EBM is defined as the integration of the best available evidence with our clinical expertise and our patients’ unique values and circumstances.4 Evidence, whether strong or weak, is never sufficient to make clinical decisions. Individual values and preferences must balance this evidence to achieve optimal shared decision making.

    Others besides Dr Healy have stated their concern that only randomised …

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