Confidential clinician-reported surveillance of adverse events among medical inpatients

J Gen Intern Med. 2000 Jul;15(7):470-7. doi: 10.1046/j.1525-1497.2000.06269.x.


Background: Although iatrogenic injury poses a significant risk to hospitalized patients, detection of adverse events (AEs) is costly and difficult.

Methods: The authors developed a confidential reporting method for detecting AEs on a medicine unit of a teaching hospital. Adverse events were defined as patient injuries. Potential adverse events (PAEs) represented errors that could have, but did not result in harm. Investigators interviewed house officers during morning rounds and by e-mail, asking them to identify obstacles to high quality care and iatrogenic injuries. They compared house officer reports with hospital incident reports and patients' medical records. A multivariate regression model identified correlates of reporting.

Results: One hundred ten events occurred, affecting 84 patients. Queries by e-mail (incidence rate ratio [IRR] = 0.16; 95% confidence interval [95% CI], 0.05 to 0.49) and on days when house officers rotated to a new service (IRR = 0.12; 95% CI, 0.02 to 0.91) resulted in fewer reports. The most commonly reported process of care problems were inadequate evaluation of the patient (16.4%), failure to monitor or follow up (12.7%), and failure of the laboratory to perform a test (12.7%). Respondents identified 29 (26. 4%) AEs, 52 (47.3%) PAEs, and 29 (26.4%) other house officer-identified quality problems. An AE occurred in 2.6% of admissions. The hospital incident reporting system detected only one house officer-reported event. Chart review corroborated 72.9% of events.

Conclusions: House officers detect many AEs among inpatients. Confidential peer interviews of front-line providers is a promising method for identifying medical errors and substandard quality.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Boston
  • Confidentiality*
  • Data Collection / methods
  • Hospitals, Teaching / statistics & numerical data*
  • Humans
  • Inpatients / statistics & numerical data*
  • Medical Errors / statistics & numerical data*
  • Population Surveillance
  • Quality Assurance, Health Care / methods*