Evidence map›Paper›PMID 32036798›Full record

ArticleInfection control and hospital epidemiology2020

The impact of an electronic medical record nudge on reducing testing for hospital-onset

Jessica R Howard-Anderson, Mary Elizabeth Sexton, Chad Robichaux, Zanthia Wiley, Jay B Varkey, Sujit Suchindran, Benjamin Albrecht, K Ashley Jones, Scott K Fridkin, Jesse T Jacob

Open access · greenAbstract readComparative Study
In one paragraph

Article in Infection control and hospital epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.5field-weighted citation impact, top 22% of its field
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

7 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Clinical decision support for gastrointestinal panel testing.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2024
    Article
  4. Article
  5. Article
  6. Review
  7. Reductions in inpatient fluoroquinolone use and postdischargeAntimicrobial stewardship & healthcare epidemiology : ASHE · 2021
    Article
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

10 authors at 2 institutions in 1 country.

Jessica R Howard-AndersonDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.ORCID 0000-0001-9924-0458
Mary Elizabeth SextonDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Chad RobichauxDepartment of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Zanthia WileyDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Jay B VarkeyDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Sujit SuchindranDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Benjamin AlbrechtDepartment of Pharmacy, Emory Healthcare, Atlanta, Georgia.
K Ashley JonesDepartment of Pharmacy, Emory Healthcare, Atlanta, Georgia.
Scott K FridkinDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Jesse T JacobDivision of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Emory University · USEmory Healthcare · US

Funding

Implementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1M
J: NRSA Training CoreTL1TR002382 · NCATS · EMORY UNIVERSITY · PI HENRY M BLUMBERG, Vasiliki Michopoulos · 2017 to 2026
$8.5M
NCATS NIH HHS TL1 TR002382NCATS NIH HHS UL1 TR002378NCEZID CDC HHS U54 CK000164
6 · The paper itself

Abstract

objectiveTo determine the effect of an electronic medical record (EMR) nudge at reducing total and inappropriate orders testing for hospital-onset Clostridioides difficile infection (HO-CDI).

designAn interrupted time series analysis of HO-CDI orders 2 years before and 2 years after the implementation of an EMR intervention designed to reduce inappropriate HO-CDI testing. Orders for C. difficile testing were considered inappropriate if the patient had received a laxative or stool softener in the previous 24 hours.

settingFour hospitals in an academic healthcare network. PATIENTS: All patients with a C. difficile order after hospital day 3.

interventionOrders for C. difficile testing in patients administered a laxative or stool softener in <24 hours triggered an EMR alert defaulting to cancellation of the order ("nudge").

resultsOf the 17,694 HO-CDI orders, 7% were inappropriate (8% prentervention vs 6% postintervention; P < .001). Monthly HO-CDI orders decreased by 21% postintervention (level-change rate ratio [RR], 0.79; 95% confidence interval [CI], 0.73-0.86), and the rate continued to decrease (postintervention trend change RR, 0.99; 95% CI, 0.98-1.00). The intervention was not associated with a level change in inappropriate HO-CDI orders (RR, 0.80; 95% CI, 0.61-1.05), but the postintervention inappropriate order rate decreased over time (RR, 0.95; 95% CI, 0.93-0.97).

conclusionAn EMR nudge to minimize inappropriate ordering for C. difficile was effective at reducing HO-CDI orders, and likely contributed to decreasing the inappropriate HO-CDI order rate after the intervention.

Indexed as

Decision Support Systems, ClinicalAcademic Medical CentersAdultAgedClostridioides difficileClostridium InfectionsCross InfectionElectronic Health RecordsFemaleHospitalsHumansMaleMedical OveruseMiddle AgedRetrospective Studies

Identifiers

PMID32036798
PMCPMC7909614
OpenAlexW3005556181

What OpenQuestion holds

Textmetadata
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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.