Examining and addressing evidence-practice gaps in cancer care: a systematic review

Implement Sci. 2014 Mar 25;9(1):37. doi: 10.1186/1748-5908-9-37.

Abstract

Background: There is increasing recognition of gaps between best scientific evidence and clinical practice. This systematic review aimed to assess the volume and scope of peer-reviewed cancer research output in the years 2000, 2005, and 2010.

Methods: Eligible papers were published in English and reported on evidence-practice gaps in cancer care. The electronic database Medline was searched for three time periods using MeSH headings and keywords. Abstracts were assessed against eligibility criteria by one reviewer and checked by a second. Papers meeting eligibility criteria were coded as data-based or non-data-based, and by cancer type of focus. All data-based papers were then further classified as descriptive studies documenting the extent of, or barriers to addressing, the evidence-practice gap; or intervention studies examining the effectiveness of strategies to reduce the evidence-practice gap.

Results: A total of 176 eligible papers were identified. The number of publications significantly increased over time, from 25 in 2000 to 100 in 2010 (p < 0.001). Of the 176 identified papers, 160 were data-based. The majority of these (n = 150) reported descriptive studies. Only 10 studies examined the effectiveness of interventions designed to reduce discrepancies between evidence and clinical practice. Of these, only one was a randomized controlled trial. Of all data-based studies, almost one-third (n = 48) examined breast cancer care.

Conclusions: While the number of publications investigating evidence-practice gaps in cancer care increased over a ten-year period, most studies continued to describe gaps between best evidence and clinical practice, rather than rigorously testing interventions to reduce the gap.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review
  • Systematic Review

MeSH terms

  • Breast Neoplasms / therapy
  • Evidence-Based Medicine / statistics & numerical data*
  • Humans
  • Neoplasms / therapy*
  • Translational Research, Biomedical / statistics & numerical data*