Making electronic prescribing alerts more effective: scenario-based experimental study in junior doctors
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作者:
Scott, Gregory P. T.
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Dept Hlth Informat Directorate, Leeds, W Yorkshire, England
Imperial Coll Healthcare NHS Trust, London, EnglandDept Hlth Informat Directorate, Leeds, W Yorkshire, England
Scott, Gregory P. T.
[1
,2
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Shah, Priya
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Dept Hlth Informat Directorate, Leeds, W Yorkshire, England
Imperial Coll Healthcare NHS Trust, London, EnglandDept Hlth Informat Directorate, Leeds, W Yorkshire, England
Shah, Priya
[1
,2
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Wyatt, Jeremy C.
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Univ Warwick, Inst Digital Healthcare, Coventry CV4 7AL, W Midlands, EnglandDept Hlth Informat Directorate, Leeds, W Yorkshire, England
Wyatt, Jeremy C.
[3
]
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Makubate, Boikanyo
[4
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Cross, Frank W.
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Dept Hlth Informat Directorate, Leeds, W Yorkshire, England
Barts & London NHS Trust, London, EnglandDept Hlth Informat Directorate, Leeds, W Yorkshire, England
Cross, Frank W.
[1
,5
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机构:
[1] Dept Hlth Informat Directorate, Leeds, W Yorkshire, England
[2] Imperial Coll Healthcare NHS Trust, London, England
[3] Univ Warwick, Inst Digital Healthcare, Coventry CV4 7AL, W Midlands, England
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.
机构:
Univ Maryland, Sch Med, Dept Med, Program Personalized & Genom Med, Baltimore, MD 21201 USAUniv Maryland, Sch Med, Dept Med, Program Personalized & Genom Med, Baltimore, MD 21201 USA
Overby, Casey Lynnette
Devine, Emily Beth
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Univ Washington, Dept Biomed Informat & Med Educ, Seattle, WA 98195 USA
Univ Washington, Dept Pharm, Seattle, WA 98195 USA
Univ Washington, Dept Hlth Serv, Seattle, WA 98195 USAUniv Maryland, Sch Med, Dept Med, Program Personalized & Genom Med, Baltimore, MD 21201 USA
Devine, Emily Beth
Abernethy, Neil
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Univ Washington, Dept Biomed Informat & Med Educ, Seattle, WA 98195 USA
Univ Washington, Dept Hlth Serv, Seattle, WA 98195 USAUniv Maryland, Sch Med, Dept Med, Program Personalized & Genom Med, Baltimore, MD 21201 USA
Abernethy, Neil
McCune, Jeannine S.
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Univ Washington, Dept Pharm, Seattle, WA 98195 USAUniv Maryland, Sch Med, Dept Med, Program Personalized & Genom Med, Baltimore, MD 21201 USA
机构:
Gachon Univ, Res Inst AI & Nursing Sci, Coll Nursing, Incheon, South KoreaGachon Univ, Res Inst AI & Nursing Sci, Coll Nursing, Incheon, South Korea
Lee, Hyeongsuk
Lee, Seungmin
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Gachon Univ, Coll Nursing, Incheon, South KoreaGachon Univ, Res Inst AI & Nursing Sci, Coll Nursing, Incheon, South Korea
Lee, Seungmin
Jo, Dami
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Gachon Univ, Coll Nursing, Incheon, South KoreaGachon Univ, Res Inst AI & Nursing Sci, Coll Nursing, Incheon, South Korea