title: What are incident reports telling us? A comparative study at two Australian hospitals of medication errors identified at audit, detected by staff and reported to an incident system.
abstract: OBJECTIVE
  To (i) compare medication errors identified at audit and observation with medication incident reports; (ii) identify differences between two hospitals in incident report frequency and medication error rates; (iii) identify prescribing error detection rates by staff.
  
  
  METHODS
  Audit of 3291 patient records at two hospitals to identify prescribing errors and evidence of their detection by staff. Medication administration errors were identified from a direct observational study of 180 nurses administering 7451 medications. Severity of errors was classified. Those likely to lead to patient harm were categorized as 'clinically important'.
  
  
  METHODS
  Two major academic teaching hospitals in Sydney, Australia.
  
  
  METHODS
  Rates of medication errors identified from audit and from direct observation were compared with reported medication incident reports.
  
  
  RESULTS
  A total of 12 567 prescribing errors were identified at audit. Of these 1.2/1000 errors (95% CI: 0.6-1.8) had incident reports. Clinically important prescribing errors (n = 539) were detected by staff at a rate of 218.9/1000 (95% CI: 184.0-253.8), but only 13.0/1000 (95% CI: 3.4-22.5) were reported. 78.1% (n = 421) of clinically important prescribing errors were not detected. A total of 2043 drug administrations (27.4%; 95% CI: 26.4-28.4%) contained ≥ 1 errors; none had an incident report. Hospital A had a higher frequency of incident reports than Hospital B, but a lower rate of errors at audit.
  
  
  CONCLUSIONS
  Prescribing errors with the potential to cause harm frequently go undetected. Reported incidents do not reflect the profile of medication errors which occur in hospitals or the underlying rates. This demonstrates the inaccuracy of using incident frequency to compare patient risk or quality performance within or across hospitals. New approaches including data mining of electronic clinical information systems are required to support more effective medication error detection and mitigation.
authors.affiliation: Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia.
authors.forename: Johanna I
authors.initials: JI
authors.lastname: Westbrook
authors.affiliation: Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia.
authors.forename: Ling
authors.initials: L
authors.lastname: Li
authors.affiliation: Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia.
authors.forename: Elin C
authors.initials: EC
authors.lastname: Lehnbom
authors.affiliation: Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia.
authors.forename: Melissa T
authors.initials: MT
authors.lastname: Baysari
authors.affiliation: Centre for Healthcare Resilience and Implementation Science, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Braithwaite
authors.affiliation: Pharmacy Department, Concord Repatriation General Hospital, Sydney 2139, Australia.
authors.forename: Rosemary
authors.initials: R
authors.lastname: Burke
authors.affiliation: Patient Safety & Quality, Clinical Governance, St Vincent's Health Network, Sydney 2010, Australia.
authors.forename: Chris
authors.initials: C
authors.lastname: Conn
authors.affiliation: Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, Sydney, and UNSW Medicine, University of New South Wales, Sydney 2052, Australia.
authors.forename: Richard O
authors.initials: RO
authors.lastname: Day
mesh_terms: D001315:Australia; D006784:Hospitals, Teaching / Q000458:organization & administration / Q000706:statistics & numerical data*; D006801:Humans; D008508:Medication Errors / Q000145:classification* / Q000706:statistics & numerical data*; D064406:Patient Harm / Q000145:classification / Q000706:statistics & numerical data; D055656:Prescriptions / Q000706:statistics & numerical data; D011787:Quality of Health Care; D012308:Risk Management / Q000458:organization & administration / Q000706:statistics & numerical data*; D017751:Safety Management / Q000458:organization & administration / Q000706:statistics & numerical data*
keywords: electronic prescribing; incident reporting; medication administration errors; medication error; safety