title: Can we rely on patients' reports of adverse events?
abstract: BACKGROUND
  Evidence suggests that patients can report a variety of adverse events (AEs) not captured by traditional methods such as a chart review. Little is known, however, about whether patient reports are useful for measuring patient safety.
  
  
  OBJECTIVE
  To examine the degree to which physician reviewers agreed that patient reports of "negative effects" constituted AEs, and to identify questionnaire items that affected reviewers' judgments.
  
  
  METHODS
  We surveyed patients discharged from Massachusetts hospitals in 2003 to elicit information about negative effects associated with hospitalization. Physician reviewers judged whether patient-reported negative effects represented AEs, and classified the severity of the event. Likelihood ratios were calculated to assess whether patient responses to questionnaire items affected reviewers' judgments.
  
  
  RESULTS
  Of the 2582 patients surveyed, 753 patients reported 1170 negative effects, and 71.2% of these effects were classified as AEs by physician reviewers. Negative effects most likely to be classified as AEs involved newly prescribed medications and changes to previously prescribed medications. Additional information elicited from follow-up survey questions modestly affected reviewers' classification of serious AEs. Negative effects reported by women, younger patients, those reporting better health status, and those not admitted through the emergency department were more likely to be classified as AEs.
  
  
  CONCLUSIONS
  Many patients were able to identify care-related AEs. Patient responses to questions about the sequelae of the events provided limited additional information for physicians to use in gauging the presence and severity of the event. Patient reports complement other incident-detection methods by providing information that is credible and unavailable from other sources.
authors.affiliation: Center for Patient Safety, Dana-Farber Cancer Institute, The Heller School for Social Policy and Management, Brandeis University, Boston, MA 02215-5450, USA. junya_zhu@dfci.harvard.edu
authors.forename: Junya
authors.initials: J
authors.lastname: Zhu
authors.forename: Sherri O
authors.initials: SO
authors.lastname: Stuver
authors.forename: Arnold M
authors.initials: AM
authors.lastname: Epstein
authors.forename: Eric C
authors.initials: EC
authors.lastname: Schneider
authors.forename: Joel S
authors.initials: JS
authors.lastname: Weissman
authors.forename: Saul N
authors.initials: SN
authors.lastname: Weingart
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016009:Chi-Square Distribution; D003625:Data Collection / Q000379:methods; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008404:Massachusetts; D019300:Medical Errors / Q000706:statistics & numerical data*; D008875:Middle Aged; D010351:Patient Discharge; D010361:Patients; D015203:Reproducibility of Results; D012650:Self Disclosure; D011795:Surveys and Questionnaires