Evidence map›Paper›PMID 28154988›Full record

ArticleJournal of digital imaging2017

Computer-Aided Reporting of Chest Radiographs: Efficient and Effective Screening in the Value-Based Imaging Era.

Michael Morris, Babak Saboury, Niketh Bandla, Christopher Toland, Christopher Meenan, Eliot Siegel, Jean Jeudy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 45% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Use of big data on the social determinants of TB to find the "missing millions".The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Article
  2. 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

7 authors at 2 institutions in 1 country.

Michael MorrisDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA. mmorris@jhu.edu.
Babak SabouryDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA.
Niketh BandlaDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA.
Christopher TolandDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA.
Christopher MeenanDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA.
Eliot SiegelDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA.
Jean JeudyDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene Street, Baltimore, MD, 21201, USA.
University of Maryland, Baltimore · USUniversity of Maryland Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the post-PACS era, mammography is unique in adopting specialized ergonomic interfaces to improve efficiency in a high volume setting. Chest radiography is also a high volume area of radiology. The authors hypothesize that applying a novel interface for chest radiography interpretation and reporting could create high productivity while maintaining quality. A custom version of the ClearCanvas open source software, EzRad, was created with a workflow re-designed specifically for tuberculosis screening chest radiographs, which utilized standardized computer generated reports. The preliminary reports from 881,792 studies evaluated by radiology residents over a nine-year period were analyzed for productivity as RVU/FTE and compared to the finalized reports from a subspecialty attending chest radiologist for accuracy. Radiology residents were able to produce 7480 RVU/FTE per year in screening chest radiography productivity when using a custom interface at a large academic medical center with a miss rate of 0.1%. Sensitivity was 77% and specificity was 99.9%. An ergonomic user interface allowed high productivity in interpretation of chest radiography for tuberculosis screening while maintaining quality.

Indexed as

Efficiency, OrganizationalWorkflowErgonomicsHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedRadiography, ThoracicReproducibility of ResultsSensitivity and SpecificitySoftwareTuberculosisBig dataComputer aided reportingProductivityScreening chest radiographTuberculosisValue based care

Identifiers

PMID28154988
PMCPMC5603434
OpenAlexW2583704902

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.