title: Errors and adverse events in family medicine: developing and validating a Canadian taxonomy of errors.
abstract: OBJECTIVE
  To develop a taxonomy of errors derived solely from the content of error reports using Canadian data from the Primary Care International Study of Medical Errors.
  
  
  METHODS
  Secondary analysis of data from a descriptive, cross-sectional, self-report survey.
  
  
  METHODS
  Community-based family medicine clinics.
  
  
  METHODS
  Family physicians.
  
  
  METHODS
  Implementation of an error-reporting system for family medicine.
  
  
  METHODS
  Type of error, type of causal factor.
  
  
  RESULTS
  Six types of errors or adverse events (administrative, communication, diagnostic, documentation, medication, and surgical or procedural) and 10 causal factors (case complexity, discontinuity of care, failure to follow protocol or accepted practice, fatigue, gap in knowledge, high workload, insufficient information on pharmacologic properties of medication, medication side effects, relationship dynamics, and structural problems) were identified.
  
  
  CONCLUSIONS
  Our taxonomy differs from that adopted by the Primary Care International Study of Medical Errors. We propose that our taxonomy is better suited for the purposes of family physicians reporting errors in Canada.
authors.affiliation: Department of Public Health Sciences, University of Toronto, Ontario.
authors.forename: Sarah
authors.initials: S
authors.lastname: Jacobs
authors.forename: Maeve
authors.initials: M
authors.lastname: O'Beirne
authors.forename: Luz Palacios
authors.initials: LP
authors.lastname: Derfiingher
authors.forename: Lucie
authors.initials: L
authors.lastname: Vlach
authors.forename: Walter
authors.initials: W
authors.lastname: Rosser
authors.forename: Neil
authors.initials: N
authors.lastname: Drummond
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D002170:Canada; D002983:Clinical Competence; D003695:Delivery of Health Care; D005194:Family Practice / Q000592:standards* / Q000639:trends; D005260:Female; D019538:Health Care Surveys; D006801:Humans; D015994:Incidence; D008297:Male; D019300:Medical Errors / Q000145:classification* / Q000706:statistics & numerical data; D015588:Observer Variation; D015203:Reproducibility of Results; D018570:Risk Assessment; D017751:Safety Management