title: Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support.
abstract: BACKGROUND
  Clinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance.
  
  
  METHODS
  We built a logistic regression model to predict alert acceptance of drug-drug interaction (DDI) alerts in three different settings. Ten variables from the clinical and human factors literature were evaluated as potential modulators of provider alert acceptance. ORs were calculated for the impact of knowledge quality, alert display, textual information, prioritization, setting, patient age, dose-dependent toxicity, alert frequency, alert level, and required acknowledgment on acceptance of the DDI alert.
  
  
  RESULTS
  50,788 DDI alerts were analyzed. Providers accepted only 1.4% of non-interruptive alerts. For interruptive alerts, user acceptance positively correlated with frequency of the alert (OR 1.30, 95% CI 1.23 to 1.38), quality of display (4.75, 3.87 to 5.84), and alert level (1.74, 1.63 to 1.86). Alert acceptance was higher in inpatients (2.63, 2.32 to 2.97) and for drugs with dose-dependent toxicity (1.13, 1.07 to 1.21). The textual information influenced the mode of reaction and providers were more likely to modify the prescription if the message contained detailed advice on how to manage the DDI.
  
  
  CONCLUSIONS
  We evaluated potential modulators of alert acceptance by assessing content and human factors issues, and quantified the impact of a number of specific factors which influence alert acceptance. This information may help improve clinical decision support systems design.
authors.affiliation: Division of General Medicine and Primary Care, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
authors.forename: Hanna M
authors.initials: HM
authors.lastname: Seidling
authors.forename: Shobha
authors.initials: S
authors.lastname: Phansalkar
authors.forename: Diane L
authors.initials: DL
authors.lastname: Seger
authors.forename: Marilyn D
authors.initials: MD
authors.lastname: Paterno
authors.forename: Shimon
authors.initials: S
authors.lastname: Shaykevich
authors.forename: Walter E
authors.initials: WE
authors.lastname: Haefeli
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
mesh_terms: D001292:Attitude to Computers; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data; D004347:Drug Interactions; D006804:Ergonomics; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008509:Medication Systems / Q000706:statistics & numerical data; D008875:Middle Aged; D015999:Multivariate Analysis; D012189:Retrospective Studies; D014481:United States; D014584:User-Computer Interface