title: Overrides of medication alerts in ambulatory care.
abstract: BACKGROUND
  Electronic prescribing systems with decision support may improve patient safety in ambulatory care by offering drug allergy and drug interaction alerts. However, preliminary studies show that clinicians override most of these alerts.
  
  
  METHODS
  We performed a retrospective analysis of 233 537 medication safety alerts generated by 2872 clinicians in Massachusetts, New Jersey, and Pennsylvania who used a common electronic prescribing system from January 1, 2006, through September 30, 2006. We used multivariate techniques to examine factors associated with alert acceptance.
  
  
  RESULTS
  A total of 6.6% of electronic prescription attempts generated alerts. Clinicians accepted 9.2% of drug interaction alerts and 23.0% of allergy alerts. High-severity interactions accounted for most alerts (61.6%); clinicians accepted high-severity alerts slightly more often than moderate- or low-severity interaction alerts (10.4%, 7.3%, and 7.1%, respectively; P < .001). Clinicians accepted 2.2% to 43.1% of high-severity interaction alerts, depending on the classes of interacting medications. In multivariable analyses, we found no difference in alert acceptance among clinicians of different specialties (P = .16). Clinicians were less likely to accept a drug interaction alert if the patient had previously received the alerted medication (odds ratio, 0.03; 95% confidence interval, 0.03-0.03).
  
  
  CONCLUSIONS
  Clinicians override most medication alerts, suggesting that current medication safety alerts may be inadequate to protect patient safety.
authors.affiliation: Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center, 330 Brookline Ave., Boston, MA 02215, USA. tisaac@bidmc.harvard.edu
authors.forename: Thomas
authors.initials: T
authors.lastname: Isaac
authors.forename: Joel S
authors.initials: JS
authors.lastname: Weissman
authors.forename: Roger B
authors.initials: RB
authors.lastname: Davis
authors.forename: Michael
authors.initials: M
authors.lastname: Massagli
authors.forename: Adrienne
authors.initials: A
authors.lastname: Cyrulik
authors.forename: Daniel Z
authors.initials: DZ
authors.lastname: Sands
authors.forename: Saul N
authors.initials: SN
authors.lastname: Weingart
mesh_terms: D000293:Adolescent; D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems; D000368:Aged; D000369:Aged, 80 and over; D000553:Ambulatory Care; D004342:Drug Hypersensitivity / Q000517:prevention & control*; D004347:Drug Interactions; D004360:Drug Therapy, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D050316:Medical Order Entry Systems; D008508:Medication Errors / Q000517:prevention & control; D008511:Medicine / Q000706:statistics & numerical data; D008875:Middle Aged; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012720:Severity of Illness Index; D013038:Specialization; D014481:United States; D055815:Young Adult