Literature DB >> 23971454

Cognitive and system factors contributing to diagnostic errors in radiology.

Cindy S Lee1, Paul G Nagy, Sallie J Weaver, David E Newman-Toker.   

Abstract

OBJECTIVE: In this article, we describe some of the cognitive and system-based sources of detection and interpretation errors in diagnostic radiology and discuss potential approaches to help reduce misdiagnoses.
CONCLUSION: Every radiologist worries about missing a diagnosis or giving a false-positive reading. The retrospective error rate among radiologic examinations is approximately 30%, with real-time errors in daily radiology practice averaging 3-5%. Nearly 75% of all medical malpractice claims against radiologists are related to diagnostic errors. As medical reimbursement trends downward, radiologists attempt to compensate by undertaking additional responsibilities to increase productivity. The increased workload, rising quality expectations, cognitive biases, and poor system factors all contribute to diagnostic errors in radiology. Diagnostic errors are underrecognized and underappreciated in radiology practice. This is due to the inability to obtain reliable national estimates of the impact, the difficulty in evaluating effectiveness of potential interventions, and the poor response to systemwide solutions. Most of our clinical work is executed through type 1 processes to minimize cost, anxiety, and delay; however, type 1 processes are also vulnerable to errors. Instead of trying to completely eliminate cognitive shortcuts that serve us well most of the time, becoming aware of common biases and using metacognitive strategies to mitigate the effects have the potential to create sustainable improvement in diagnostic errors.

Entities:  

Mesh:

Year:  2013        PMID: 23971454     DOI: 10.2214/AJR.12.10375

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  63 in total

1.  What is the relation between number of sessions worked and productivity of radiologists-a pilot study?

Authors:  Shah H M Khan; William P Hedges
Journal:  J Digit Imaging       Date:  2016-04       Impact factor: 4.056

Review 2.  Errors in imaging patients in the emergency setting.

Authors:  Antonio Pinto; Alfonso Reginelli; Fabio Pinto; Giuseppe Lo Re; Federico Midiri; Carlo Muzj; Luigia Romano; Luca Brunese
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3.  A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Authors:  Naji Khosravan; Haydar Celik; Baris Turkbey; Elizabeth C Jones; Bradford Wood; Ulas Bagci
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4.  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

5.  Radiographers' performance in chest X-ray interpretation: the Nigerian experience.

Authors:  E U Ekpo; N O Egbe; B E Akpan
Journal:  Br J Radiol       Date:  2015-05-12       Impact factor: 3.039

6.  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

7.  Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks.

Authors:  Mauro Annarumma; Samuel J Withey; Robert J Bakewell; Emanuele Pesce; Vicky Goh; Giovanni Montana
Journal:  Radiology       Date:  2019-01-22       Impact factor: 11.105

8.  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

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

Authors:  Zahra Hussain
Journal:  Proc Natl Acad Sci U S A       Date:  2020-09-30       Impact factor: 11.205

Review 10.  Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

Authors:  Steven E Dilsizian; Eliot L Siegel
Journal:  Curr Cardiol Rep       Date:  2014-01       Impact factor: 2.931

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