Evidence map›Paper›PMID 21836158›Full record

Trial reportJournal of the American Medical Informatics Association : JAMIA

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

Abstract readComparative StudyRandomized Controlled Trial
In one paragraph

Trial report in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. A Systematic Review of Clinical Decision Support Systems for Clinical Oncology Practice.Journal of the National Comprehensive Cancer Network : JNCCN · 2019
    Pooled it
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  14. Modeling information flows in clinical decision support: key insights for enhancing system effectiveness.Journal of the American Medical Informatics Association : JAMIA · 2016
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  18. Prescribing pattern of interns: Time for new interventions.Journal of basic and clinical pharmacy · 2014
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Gregory P T ScottDepartment of Health Informatics Directorate, Leeds, West Yorkshire, UK. gregory.scott99@imperial.ac.uk
Priya Shah
Jeremy C Wyatt
Boikanyo Makubate
Frank W Cross

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveExpert 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.

designA 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.

resultsParticipants 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).

conclusionsBoth 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.

Indexed as

Electronic PrescribingMedical Order Entry SystemsDecision Support Systems, ClinicalHumansMedical Staff, HospitalMedication ErrorsReminder Systems

Identifiers

PMID21836158
PMCPMC3197997

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.