Evidence map›Paper›PMID 21571746›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support.

Hanna M Seidling, Shobha Phansalkar, Diane L Seger, Marilyn D Paterno, Shimon Shaykevich, Walter E Haefeli, David W Bates

Abstract readMulticenter Study
In one paragraph

Article 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 59 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 1 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

59 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Impact of electronic reminders on venous thromboprophylaxis after admissions and transfers.Journal of the American Medical Informatics Association : JAMIA · 2014
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  8. Review
  9. Article
  10. Article
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  13. Clinical decision support systems in community pharmacies: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. The potential for leveraging machine learning to filter medication alerts.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  19. Article
  20. Review
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

7 authors.

Hanna M SeidlingDivision of General Medicine and Primary Care, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
Shobha Phansalkar
Diane L Seger
Marilyn D Paterno
Shimon Shaykevich
Walter E Haefeli
David W Bates

Funding

IMPROVING SAFETY BY COMPUTERIZING OUTPATIENT PRESCRIBINGU18HS011169 · AHRQ · BRIGHAM AND WOMEN'S HOSPITAL · PI BATES, DAVID W · 2000 to 2002
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AHRQ HHS HS11169-01AHRQ HHS U18 HS011169
6 · The paper itself

Abstract

backgroundClinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance.

methodsWe built a logistic regression model to predict alert acceptance of drug-drug interaction (DDI) alerts in three different settings. Ten variables from the clinical and human factors literature were evaluated as potential modulators of provider alert acceptance. ORs were calculated for the impact of knowledge quality, alert display, textual information, prioritization, setting, patient age, dose-dependent toxicity, alert frequency, alert level, and required acknowledgment on acceptance of the DDI alert.

results50,788 DDI alerts were analyzed. Providers accepted only 1.4% of non-interruptive alerts. For interruptive alerts, user acceptance positively correlated with frequency of the alert (OR 1.30, 95% CI 1.23 to 1.38), quality of display (4.75, 3.87 to 5.84), and alert level (1.74, 1.63 to 1.86). Alert acceptance was higher in inpatients (2.63, 2.32 to 2.97) and for drugs with dose-dependent toxicity (1.13, 1.07 to 1.21). The textual information influenced the mode of reaction and providers were more likely to modify the prescription if the message contained detailed advice on how to manage the DDI.

conclusionWe evaluated potential modulators of alert acceptance by assessing content and human factors issues, and quantified the impact of a number of specific factors which influence alert acceptance. This information may help improve clinical decision support systems design.

Indexed as

Attitude to ComputersDecision Support Systems, ClinicalErgonomicsMedication SystemsUser-Computer InterfaceDrug InteractionsFemaleHumansLogistic ModelsMaleMiddle AgedMultivariate AnalysisRetrospective StudiesUnited States

Identifiers

PMID21571746
PMCPMC3128393

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.