Literature DB >> 26466178

Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction.

Michael A Bruno1, Eric A Walker1, Hani H Abujudeh1.   

Abstract

Arriving at a medical diagnosis is a highly complex process that is extremely error prone. Missed or delayed diagnoses often lead to patient harm and missed opportunities for treatment. Since medical imaging is a major contributor to the overall diagnostic process, it is also a major potential source of diagnostic error. Although some diagnoses may be missed because of the technical or physical limitations of the imaging modality, including image resolution, intrinsic or extrinsic contrast, and signal-to-noise ratio, most missed radiologic diagnoses are attributable to image interpretation errors by radiologists. Radiologic interpretation cannot be mechanized or automated; it is a human enterprise based on complex psychophysiologic and cognitive processes and is itself subject to a wide variety of error types, including perceptual errors (those in which an important abnormality is simply not seen on the images) and cognitive errors (those in which the abnormality is visually detected but the meaning or importance of the finding is not correctly understood or appreciated). The overall prevalence of radiologists' errors in practice does not appear to have changed since it was first estimated in the 1960s. The authors review the epidemiology of errors in diagnostic radiology, including a recently proposed taxonomy of radiologists' errors, as well as research findings, in an attempt to elucidate possible underlying causes of these errors. The authors also propose strategies for error reduction in radiology. On the basis of current understanding, specific suggestions are offered as to how radiologists can improve their performance in practice. © RSNA, 2015.

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Year:  2015        PMID: 26466178     DOI: 10.1148/rg.2015150023

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  72 in total

1.  Longitudinal evolution of CT and MRI LI-RADS v2014 category 1, 2, 3, and 4 observations.

Authors:  Cheng William Hong; Charlie C Park; Adrija Mamidipalli; Jonathan C Hooker; Soudabeh Fazeli Dehkordy; Saya Igarashi; Mohanad Alhumayed; Yuko Kono; Rohit Loomba; Tanya Wolfson; Anthony Gamst; Paul Murphy; Claude B Sirlin
Journal:  Eur Radiol       Date:  2019-02-26       Impact factor: 5.315

2.  Features of diffuse gliomas that are misdiagnosed on initial neuroimaging: a case control study.

Authors:  M D Maldonado; P Batchala; D Ornan; C Fadul; D Schiff; J N Itri; R Jain; S H Patel
Journal:  J Neurooncol       Date:  2018-06-29       Impact factor: 4.130

3.  Radiology resident MR and CT image analysis skill assessment using an interactive volumetric simulation tool - the RadioLOG project.

Authors:  Pedro Augusto Gondim Teixeira; Romain Cendre; Gabriela Hossu; Christophe Leplat; Jacques Felblinger; Alain Blum; Marc Braun
Journal:  Eur Radiol       Date:  2016-05-10       Impact factor: 5.315

4.  Bias in Neuroradiology Peer Review: Impact of a "Ding" on "Dinging" Others.

Authors:  P Charkhchi; B Wang; B Caffo; D M Yousem
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-06       Impact factor: 3.825

5.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

6.  Diagnostic errors when interpreting abdominopelvic computed tomography: a pictorial review.

Authors:  Seong Jong Yun; Hyun Cheol Kim; Dal Mo Yang; Sang Won Kim; Sun Jung Rhee; Sung Eun Ahn
Journal:  Br J Radiol       Date:  2017-03-31       Impact factor: 3.039

7.  Increasing display luminance as a means to enhance interpretation accuracy and efficiency when reducing full-field digital mammography dose.

Authors:  Elizabeth A Krupinski
Journal:  J Med Imaging (Bellingham)       Date:  2018-07-27

8.  Deep learning in biomedicine.

Authors:  Michael Wainberg; Daniele Merico; Andrew Delong; Brendan J Frey
Journal:  Nat Biotechnol       Date:  2018-09-06       Impact factor: 54.908

9.  Risk Factors for Perceptual-versus-Interpretative Errors in Diagnostic Neuroradiology.

Authors:  S H Patel; C L Stanton; S G Miller; J T Patrie; J N Itri; T M Shepherd
Journal:  AJNR Am J Neuroradiol       Date:  2019-07-11       Impact factor: 3.825

10.  Radiologist errors by modality, anatomic region, and pathology for 1.6 million exams: what we have learned.

Authors:  Christine Lamoureux; Tarek N Hanna; Devin Sprecher; Scott Weber; Edward Callaway
Journal:  Emerg Radiol       Date:  2021-07-30
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