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

Gregory P.T. Scott, Priya Shah, Jeremy C. Wyatt, Boikanyo Makubate, Frank W. Cross

Research output: Contribution to journalArticle

38 Citations (Scopus)

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 nonmodal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.

Original languageEnglish
Pages (from-to)789-798
Number of pages10
JournalJournal of the American Medical Informatics Association
Volume18
Issue number6
DOIs
Publication statusPublished - Nov 17 2011

Fingerprint

Electronic Prescribing
Clinical Decision Support Systems
Outcome Assessment (Health Care)
Interviews

All Science Journal Classification (ASJC) codes

  • Health Informatics

Cite this

@article{0844be41361142dfa6807c2e7557f07b,
title = "Making electronic prescribing alerts more effective: Scenario-based experimental study in junior doctors",
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 nonmodal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.",
author = "Scott, {Gregory P.T.} and Priya Shah and Wyatt, {Jeremy C.} and Boikanyo Makubate and Cross, {Frank W.}",
year = "2011",
month = "11",
day = "17",
doi = "10.1136/amiajnl-2011-000199",
language = "English",
volume = "18",
pages = "789--798",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
publisher = "Oxford University Press",
number = "6",

}

Making electronic prescribing alerts more effective : Scenario-based experimental study in junior doctors. / Scott, Gregory P.T.; Shah, Priya; Wyatt, Jeremy C.; Makubate, Boikanyo; Cross, Frank W.

In: Journal of the American Medical Informatics Association, Vol. 18, No. 6, 17.11.2011, p. 789-798.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Making electronic prescribing alerts more effective

T2 - Scenario-based experimental study in junior doctors

AU - Scott, Gregory P.T.

AU - Shah, Priya

AU - Wyatt, Jeremy C.

AU - Makubate, Boikanyo

AU - Cross, Frank W.

PY - 2011/11/17

Y1 - 2011/11/17

N2 - 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 nonmodal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.

AB - 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 nonmodal alerts. This study provides new evidence about the relative effects of modal and non-modal alerts on prescribing outcomes.

UR - http://www.scopus.com/inward/record.url?scp=81055123996&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=81055123996&partnerID=8YFLogxK

U2 - 10.1136/amiajnl-2011-000199

DO - 10.1136/amiajnl-2011-000199

M3 - Article

C2 - 21836158

AN - SCOPUS:81055123996

VL - 18

SP - 789

EP - 798

JO - Journal of the American Medical Informatics Association : JAMIA

JF - Journal of the American Medical Informatics Association : JAMIA

SN - 1067-5027

IS - 6

ER -