Making electronic prescribing alerts more effective: scenario-based experimental study in junior doctors

J Am Med Inform Assoc. 2011 Nov-Dec;18(6):789-98. doi: 10.1136/amiajnl-2011-000199. Epub 2011 Aug 11.


Objective: Expert authorities recommend clinical decision support systems to reduce prescribing error rates, yet large numbers of insignificant on-screen alerts presented in modal dialog boxes persistently interrupt clinicians, limiting the effectiveness of these systems. This study compared the impact of modal and non-modal electronic (e-) prescribing alerts on prescribing error rates, to help inform the design of clinical decision support systems.

Design: A randomized study of 24 junior doctors each performing 30 simulated prescribing tasks in random order with a prototype e-prescribing system. Using a within-participant design, doctors were randomized to be shown one of three types of e-prescribing alert (modal, non-modal, no alert) during each prescribing task.

Measurements: The main outcome measure was prescribing error rate. Structured interviews were performed to elicit participants' preferences for the prescribing alerts and their views on clinical decision support systems.

Results: Participants exposed to modal alerts were 11.6 times less likely to make a prescribing error than those not shown an alert (OR 11.56, 95% CI 6.00 to 22.26). Those shown a non-modal alert were 3.2 times less likely to make a prescribing error (OR 3.18, 95% CI 1.91 to 5.30) than those not shown an alert. The error rate with non-modal alerts was 3.6 times higher than with modal alerts (95% CI 1.88 to 7.04).

Conclusions: Both kinds of e-prescribing alerts significantly reduced prescribing error rates, but modal alerts were over three times more effective than non-modal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.

Publication types

  • Comparative Study
  • Randomized Controlled Trial

MeSH terms

  • Decision Support Systems, Clinical
  • Electronic Prescribing*
  • Humans
  • Medical Order Entry Systems*
  • Medical Staff, Hospital
  • Medication Errors / prevention & control*
  • Medication Errors / statistics & numerical data
  • Reminder Systems