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. 2011 Nov-Dec;18(6):760-6.
doi: 10.1136/amiajnl-2010-000006. Epub 2011 Jun 22.

How to improve the delivery of medication alerts within computerized physician order entry systems: an international Delphi study

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How to improve the delivery of medication alerts within computerized physician order entry systems: an international Delphi study

Daniel Riedmann et al. J Am Med Inform Assoc. 2011 Nov-Dec.

Abstract

Objectives: To determine what information can be helpful in prioritizing and presenting medication alerts according to the context of the clinical situation. To assess the usefulness of different ways of delivering medication alerts to the user.

Design: An international Delphi study with two quantitative rounds. 69 researchers with expertise in computerized physician order entry (CPOE) systems were asked to estimate the usefulness of 20 possible context factors, and to assess the potential impact of six innovative ways of delivering alert information on adverse drug event (ADE) rates.

Results: Participants identified the following top five context information items (in descending order of usefulness): (1) severity of the effect of the ADE the alert refers to; (2) clinical status of the patient; (3) probability of occurrence of the ADE the alert refers to; (4) risk factors of the patient; and (5) strength of evidence on which the alert is built. The ways of delivering alert information with the highest estimated ADE reduction potential are active alerting, proactive prescription simulation and a patient medication module that gives patient-oriented alert information.

Limitations: Most participants had a research-oriented focus; therefore the results may not reflect the opinions of CPOE users or CPOE implementers.

Conclusion: The study results may provide CPOE system developers and healthcare institutions with information on how to design more effective alert mechanisms.

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Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Percentage of participants who found that a context factor is useful to prioritize and filter alerts (n=69).
Figure 2
Figure 2
Answers to the question: ‘Do you think that this tool has the potential to prevent ADE?’ (n=69). ADE, adverse drug event.
Figure 3
Figure 3
Answer to the question: ‘From 100 preventable ADE, how many could be avoided by the tool?’ (n=69). ADE, adverse drug event.

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