title: The safety of electronic prescribing: manifestations, mechanisms, and rates of system-related errors associated with two commercial systems in hospitals.
abstract: OBJECTIVE
  To compare the manifestations, mechanisms, and rates of system-related errors associated with two electronic prescribing systems (e-PS). To determine if the rate of system-related prescribing errors is greater than the rate of errors prevented.
  
  
  METHODS
  Audit of 629 inpatient admissions at two hospitals in Sydney, Australia using the CSC MedChart and Cerner Millennium e-PS. System related errors were classified by manifestation (eg, wrong dose), mechanism, and severity. A mechanism typology comprised errors made: selecting items from drop-down menus; constructing orders; editing orders; or failing to complete new e-PS tasks. Proportions and rates of errors by manifestation, mechanism, and e-PS were calculated.
  
  
  RESULTS
  42.4% (n=493) of 1164 prescribing errors were system-related (78/100 admissions). This result did not differ by e-PS (MedChart 42.6% (95% CI 39.1 to 46.1); Cerner 41.9% (37.1 to 46.8)). For 13.4% (n=66) of system-related errors there was evidence that the error was detected prior to study audit. 27.4% (n=135) of system-related errors manifested as timing errors and 22.5% (n=111) wrong drug strength errors. Selection errors accounted for 43.4% (34.2/100 admissions), editing errors 21.1% (16.5/100 admissions), and failure to complete new e-PS tasks 32.0% (32.0/100 admissions). MedChart generated more selection errors (OR=4.17; p=0.00002) but fewer new task failures (OR=0.37; p=0.003) relative to the Cerner e-PS. The two systems prevented significantly more errors than they generated (220/100 admissions (95% CI 180 to 261) vs 78 (95% CI 66 to 91)).
  
  
  CONCLUSIONS
  System-related errors are frequent, yet few are detected. e-PS require new tasks of prescribers, creating additional cognitive load and error opportunities. Dual classification, by manifestation and mechanism, allowed identification of design features which increase risk and potential solutions. e-PS designs with fewer drop-down menu selections may reduce error risk.
authors.affiliation: Faculty of Medicine, Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, University of New South Wales, Sydney, New South Wales, Australia.
authors.forename: Johanna I
authors.initials: JI
authors.lastname: Westbrook
authors.forename: Melissa T
authors.initials: MT
authors.lastname: Baysari
authors.forename: Ling
authors.initials: L
authors.lastname: Li
authors.forename: Rosemary
authors.initials: R
authors.lastname: Burke
authors.forename: Katrina L
authors.initials: KL
authors.lastname: Richardson
authors.forename: Richard O
authors.initials: RO
authors.lastname: Day
mesh_terms: D055695:Electronic Prescribing; D004868:Equipment Failure; D006784:Hospitals, Teaching; D006801:Humans; D008485:Medical Audit; D050316:Medical Order Entry Systems; D008508:Medication Errors / Q000517:prevention & control / Q000706:statistics & numerical data*; D008510:Medication Systems, Hospital; D009517:New South Wales
keywords: CPOE; Clinical information systems; Information technology; Prescribing errors; Unintended consequences