Evidence map›Paper›PMID 27478598›Full record

ArticleClinical kidney journal2016

Provider acceptance of an automated electronic alert for acute kidney injury.

Janice Oh, Joshua R Bia, Muhamad Ubaid-Ullah, Jeffrey M Testani, Francis Perry Wilson

Abstract read
In one paragraph

Article in Clinical kidney journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. eHealth interventions for people with chronic kidney disease.The Cochrane database of systematic reviews · 2019
    Pooled it
  2. Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2017
    Pooled it
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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.

Janice OhTulane School of Medicine , New Orleans, LA , USA.
Joshua R BiaProgram of Applied Translational Research , Yale School of Medicine , New Haven, CT , USA.
Muhamad Ubaid-UllahNephrology Associates of Northern Indiana , Fort Wayne, IN , USA.
Jeffrey M TestaniNephrology Associates of Northern Indiana , Fort Wayne, IN , USA.
Francis Perry WilsonNephrology Associates of Northern Indiana, Fort Wayne, IN, USA; Veterans Affairs Medical Center, West Haven, CT, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Mediators & prognostic value of muscle mass & function in chronic kidney diseaseK23DK097201 · NIDDK · YALE UNIVERSITY · PI WILSON, FRANCIS PERRY · 2013 to 2017
$916k
Cardio-renal phenotype and prognosis in chronic heart failureK23HL114868 · NHLBI · YALE UNIVERSITY · PI TESTANI, JEFFREY M · 2012 to 2017
$845k
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS K23 HL114868NIDDK NIH HHS K23 DK097201
6 · The paper itself

Abstract

backgroundClinical decision support systems, including electronic alerts, ideally provide immediate and relevant patient-specific information to improve clinical decision-making. Despite the growing capabilities of such alerts in conjunction with an expanding electronic medical record, there is a paucity of information regarding their perceived usefulness. We surveyed healthcare providers' opinions concerning the practicality and efficacy of a specific text-based automated electronic alert for acute kidney injury (AKI) in a single hospital during a randomized trial of AKI alerts.

methodsProviders who had received at least one electronic AKI alert in the previous 6 months, as part of a separate randomized controlled trial (clinicaltrials.gov #01862419), were asked to complete a survey concerning their opinions about this specific AKI alert system. Individual approval of the alert system was defined by a provider's desire to continue receiving the alert after termination of the trial.

resultsA total of 98 individuals completed the survey, including 62 physicians, 27 pharmacists and 7 non-physician providers. Sixty-nine percent of responders approved the alert, with no significant difference among the various professions (P = 0.28). Alert approval was strongly correlated with the belief that the alerts improved patient care (P < 0.0001), and negatively correlated with the belief that alerts did not provide novel information (P = 0.0001). With each additional 30 days of trial duration, odds of approval decreased by 20% (3-35%) (P = 0.02).

conclusionsThe alert system was generally well received, although approval waned with time. Approval was correlated with the belief that this type of alert improved patient care. These findings suggest that perceived efficacy is critical to the success of future alert trials.

Indexed as

acute kidney injuryalertalert fatigueapprovalclinical decision support

Identifiers

PMID27478598
PMCPMC4957729

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.