title: Optimal search filters for detecting quality improvement studies in Medline.
abstract: BACKGROUND
  As the knowledge translation and comparative effectiveness research agendas gain momentum, we can expect more evidence on which to base quality improvement (QI) programmes. Unaided searches for such content in the literature, however, are likely to be daunting, with searches missing key articles while mainly retrieving articles that are irrelevant to the question being asked. The objective of this study was to develop and validate optimal Medline search filters for retrieving original and review articles about clinical QI.
  
  
  METHODS
  Analytical survey in the McMaster Clinical Hedges database and Health Knowledge Refinery (HKR) of 161 clinical journals to determine the operating characteristics of QI search filters developed by computerised combinations of terms selected to detect original QI studies and systematic reviews meeting basic methodological criteria for scientific merit. Results from a derivation random subset of articles were tested in a validation random subset.
  
  
  RESULTS
  The Clinical Hedges QI database contained 49,233 citations of which 471 (0.96%) were original or review QI studies; of those, 282 (60%) were methodologically sound. Combinations of search terms reached peak sensitivities of 100% at a specificity of 89.3% for detecting methodologically sound original and review QI studies, and sensitivities of 97.6% at a specificity of 53.0% for detecting all original and review QI studies independent of rigour. Operating characteristics of the search filters derived in the development database worked similarly in the validation database, without statistical differences.
  
  
  CONCLUSIONS
  New empirically derived Medline search filters have been validated to optimise retrieval of original and review QI articles.
authors.affiliation: Health Information Research Unit, McMaster University, Hamilton, Ontario Canada.
authors.forename: N L
authors.initials: NL
authors.lastname: Wilczynski
authors.forename: R B
authors.initials: RB
authors.lastname: Haynes
mesh_terms: D016239:MEDLINE; D011785:Quality Assurance, Health Care; D057386:Search Engine / Q000379:methods*