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Randomized Controlled Trial
. 2011 Nov-Dec;18(6):789-98.
doi: 10.1136/amiajnl-2011-000199. Epub 2011 Aug 11.

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

Gregory P T Scott  1 , Priya Shah, Jeremy C Wyatt, Boikanyo Makubate, Frank W Cross
Affiliations
Randomized Controlled Trial

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

Gregory P T Scott et al. J Am Med Inform Assoc. 2011 Nov-Dec.

Abstract

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.

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Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Randomization of participants to alert type assignment group.
Figure 2
Figure 2
Prototype e-prescribing system showing modal alert.
Figure 3
Figure 3
Prototype e-prescribing system showing non-modal alert.
Figure 4
Figure 4
Examples of scenarios.
Figure 5
Figure 5
Sequence of possible events and outcomes for each scenario.
Figure 6
Figure 6
Distribution of error frequency according to alert type (randomized set).
See this image and copyright information in PMC

References

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