Literature DB >> 29194009

Bias in Radiology: The How and Why of Misses and Misinterpretations.

Lindsay P Busby1, Jesse L Courtier1, Christine M Glastonbury1.   

Abstract

Medical errors are a leading cause of morbidity and mortality in the medical field and are substantial contributors to medical costs. Radiologists play an integral role in the diagnosis and care of patients and, given that those in this field interpret millions of examinations annually, may therefore contribute to diagnostic errors. Errors can be categorized as a "miss" when a primary or critical finding is not observed or as a "misinterpretation" when errors in interpretation lead to an incorrect diagnosis. In this article, the authors describe the cognitive causes of such errors in diagnostic medicine, specifically in radiology. Recognizing the cognitive processes that radiologists use while interpreting images should improve one's awareness of the inherent biases that can impact decision making. The authors review the common biases that impact clinical decisions, as well as strategies to counteract or minimize the potential for misdiagnosis. System-level processes that can be implemented to minimize cognitive errors are reviewed, as well as ways to implement personal changes to minimize cognitive errors in daily practice. ©RSNA, 2017.

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Mesh:

Year:  2017        PMID: 29194009      PMCID: PMC5790309          DOI: 10.1148/rg.2018170107

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


  40 in total

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6.  An integrated model of clinical reasoning: dual-process theory of cognition and metacognition.

Authors:  James A Marcum
Journal:  J Eval Clin Pract       Date:  2012-10       Impact factor: 2.431

7.  Fool me twice: delayed diagnoses in radiology with emphasis on perpetuated errors.

Authors:  Young W Kim; Liem T Mansfield
Journal:  AJR Am J Roentgenol       Date:  2014-03       Impact factor: 3.959

8.  The radiologist's workflow environment: evaluation of disruptors and potential implications.

Authors:  John-Paul J Yu; Akash P Kansagra; John Mongan
Journal:  J Am Coll Radiol       Date:  2014-04-26       Impact factor: 5.532

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Review 10.  Interpretive Error in Radiology.

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Journal:  AJR Am J Roentgenol       Date:  2016-12-27       Impact factor: 3.959

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  32 in total

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2.  Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging.

Authors:  M T Duong; J D Rudie; J Wang; L Xie; S Mohan; J C Gee; A M Rauschecker
Journal:  AJNR Am J Neuroradiol       Date:  2019-07-25       Impact factor: 3.825

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

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Review 5.  The CT scout view: complementary value added to abdominal CT interpretation.

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6.  An expert advantage in detecting unfamiliar visual signals in noise.

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7.  Review of learning opportunity rates: correlation with radiologist assignment, patient type and exam priority.

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8.  Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study.

Authors:  Jeffrey D Rudie; Andreas M Rauschecker; Long Xie; Jiancong Wang; Michael Tran Duong; Emmanuel J Botzolakis; Asha Kovalovich; John M Egan; Tessa Cook; R Nick Bryan; Ilya M Nasrallah; Suyash Mohan; James C Gee
Journal:  Radiol Artif Intell       Date:  2020-09-23

9.  Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern.

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10.  PE MIMICS: a structured approach for the emergency radiologist in the evaluation of chest pain.

Authors:  P J Dempsey; A Yates; J W Power; M C Murphy; J P Ko; B Hutchinson
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