Evidence map›Paper›PMID 31995835›Full record

ArticleApplied clinical informatics2020

Detection and Remediation of Misidentification Errors in Radiology Examination Ordering.

Scott E Sheehan, Nasia Safdar, Hardeep Singh, Dean F Sittig, Michael A Bruno, Kelli Keller, Samantha Kinnard, Michael C Brunner

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Blind spots in brain imaging: a pictorial essay.Quantitative imaging in medicine and surgery · 2025
    Review
  4. Article
  5. 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

8 authors at 4 institutions in 1 country.

Scott E SheehanDepartment of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
Nasia SafdarDepartment of Medicine, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States.
Hardeep SinghCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center and Department of Medicine, Baylor College of Medicine, Houston, Texas, United States.
Dean F SittigSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States.
Michael A BrunoDepartment of Radiology, Penn State Hershey, Hershey, Pennsylvania, United States.
Kelli KellerDepartment of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
Samantha KinnardDepartment of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
Michael C BrunnerDepartment of Radiology, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States.
William S. Middleton Memorial Veterans Hospital · USCenter for Innovation · USPenn State Milton S. Hershey Medical Center · USThe University of Texas Health Science Center at Houston · US

Funding

AHRQ HHS R01 HS022087AHRQ HHS R18 HS017820
6 · The paper itself

Abstract

backgroundDespite progress in patient safety, misidentification errors in radiology such as ordering imaging on the wrong anatomic side persist. If undetected, these errors can cause patient harm for multiple reasons, in addition to producing erroneous electronic health records (EHR) data.

objectivesWe describe the pilot testing of a quality improvement methodology using electronic trigger tools and preimaging checklists to detect "wrong-side" misidentification errors in radiology examination ordering, and to measure staff adherence to departmental policy in error remediation.

methodsWe retrospectively applied and compared two methods for the detection of "wrong-side" misidentification errors among a cohort of all imaging studies ordered during a 1-year period (June 1, 2015-May 31, 2016) at our tertiary care hospital. Our methods included: (1) manual review of internal quality improvement spreadsheet records arising from the prospective performance of preimaging safety checklists, and (2) automated error detection via the development and validation of an electronic trigger tool which identified discrepant side indications within EHR imaging orders.

resultsOur combined methods detected misidentification errors in 6.5/1,000 of study cohort imaging orders. Our trigger tool retrospectively identified substantially more misidentification errors than were detected prospectively during preimaging checklist performance, with a high positive predictive value (PPV: 88.4%, 95% confidence interval: 85.4-91.4). However, two third of errors detected during checklist performance were not detected by the trigger tool, and checklist-detected errors were more often appropriately resolved (

conclusionOur trigger tool enabled the detection of substantially more imaging ordering misidentification errors than preimaging safety checklists alone, with a high PPV. Many errors were only detected by the preimaging checklist; however, suggesting that additional trigger tools may need to be developed and used in conjunction with checklist-based methods to ensure patient safety.

Indexed as

Medical ErrorsRadiologyAlgorithmsChecklistElectronic Health RecordsHealth PersonnelHumansMagnetic Resonance ImagingPatient Safety

Identifiers

PMID31995835
PMCPMC6989264
OpenAlexW3003644742

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.