Mapping the limits of safety reporting systems in health care--what lessons can we actually learn?

Med J Aust. 2011 Jun 20;194(12):635-9. doi: 10.5694/j.1326-5377.2011.tb03146.x.


Objectives: To assess the utility of Australian health care incident reporting systems and determine the depth of information available within a typical system.

Design and setting: Incidents relating to patient misidentification occurring between 2004 and 2008 were selected from a sample extracted from a number of Australian health services' incident reporting systems using a manual search function.

Main outcome measures: Incident type, aetiology (error type) and recovery (error-detection mechanism). Analyses were performed to determine category saturation.

Results: All 487 selected incidents could be classified according to incident type. The most prevalent incident type was medication being administered to the wrong patient (25.7%, 125), followed by incidents where a procedure was performed on the wrong patient (15.2%, 74) and incidents where an order for pathology or medical imaging was mislabelled (7.0%, 34). Category saturation was achieved quickly, with about half the total number of incident types identified in the first 13.5% of the incidents. All 43 incident types were classified within 76.2% of the dataset. Fifty-two incident reports (10.7%) included sufficient information to classify specific incident aetiology, and 288 reports (59.1%) had sufficient detailed information to classify a specific incident recovery mechanism.

Conclusions: Incident reporting systems enable the classification of the surface features of an incident and identify common incident types. However, current systems provide little useful information on the underlying aetiology or incident recovery functions. Our study highlights several limitations of incident reporting systems, and provides guidance for improving the use of such systems in quality and safety improvement.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Australia
  • Humans
  • Inpatients / statistics & numerical data
  • Medical Errors* / prevention & control
  • Medical Errors* / psychology
  • Patient Identification Systems / organization & administration
  • Patient Identification Systems / statistics & numerical data
  • Quality of Health Care / standards
  • Risk Management* / methods
  • Risk Management* / standards
  • Safety Management* / methods
  • Safety Management* / organization & administration