Can we rely on patients' reports of adverse events?

Med Care. 2011 Oct;49(10):948-55. doi: 10.1097/MLR.0b013e31822047a8.

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.

Objectives: 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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Chi-Square Distribution
  • Data Collection / methods
  • Female
  • Humans
  • Likelihood Functions
  • Logistic Models
  • Male
  • Massachusetts
  • Medical Errors / statistics & numerical data*
  • Middle Aged
  • Patient Discharge
  • Patients*
  • Reproducibility of Results
  • Self Disclosure*
  • Surveys and Questionnaires