Evidence map›Paper›PMID 32377679›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Varying rates of patient identity verification when using computerized provider order entry.

Emilie Fortman, A Zachary Hettinger, Jessica L Howe, Allan Fong, Zoe Pruitt, Kristen Miller, Raj M Ratwani

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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.

Emilie FortmanGeorgetown University School of Medicine, Washington, DC, USA.
A Zachary HettingerGeorgetown University School of Medicine, Washington, DC, USA.
Jessica L HoweMedStar Health National Center for Human Factors in Healthcare, Washington, DC, USA.
Allan FongMedStar Health National Center for Human Factors in Healthcare, Washington, DC, USA.
Zoe PruittMedStar Health National Center for Human Factors in Healthcare, Washington, DC, USA.
Kristen MillerGeorgetown University School of Medicine, Washington, DC, USA.
Raj M RatwaniGeorgetown University School of Medicine, Washington, DC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe sought to determine rates of computerized provider order entry (CPOE) patient identity verification and when and where in the ordering process verification occurred. MATERIALS AND

methodsFifty-five physicians from 4 healthcare systems completed simulated patient scenarios using their respective CPOE system (Epic or Cerner). Eye movements were recorded and analyzed.

resultsAcross all participants patient id was verified significantly more often than not (62.4% vs 37.6%). Vendor A had significantly higher verification rates than not; vendor B had no difference. Participants using vendor A verified information significantly more often before signing the order than after (88.4% vs 11.6%); there was no difference in vendor B. The banner bar was the most frequent verification location. DISCUSSION: Factors such as CPOE design, physician training, and the use of a simulated methodology may be impacting verification rates.

conclusionsVerification rates vary by CPOE product, and this can have patient safety consequences.

Indexed as

Medical Order Entry SystemsPatient Identification SystemsPatient SafetyDelivery of Health CareHumansPhysiciansSoftwareTime and Motion Studieselectronic health recordspatient identificationpatient safety

Identifiers

PMID32377679
PMCPMC7647277

What OpenQuestion holds

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Registered trials

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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.