Literature DB >> 30303801

Fundamentals of Diagnostic Error in Imaging.

Jason N Itri1, Rafel R Tappouni1, Rachel O McEachern1, Arthur J Pesch1, Sohil H Patel1.   

Abstract

Imaging plays a pivotal role in the diagnostic process for many patients. With estimates of average diagnostic error rates ranging from 3% to 5%, there are approximately 40 million diagnostic errors involving imaging annually worldwide. The potential to improve diagnostic performance and reduce patient harm by identifying and learning from these errors is substantial. Yet these relatively high diagnostic error rates have persisted in our field despite decades of research and interventions. It may often seem as if diagnostic errors in radiology occur in a haphazard fashion. However, diagnostic problem solving in radiology is not a mysterious black box, and diagnostic errors are not random occurrences. Rather, diagnostic errors are predictable events with readily identifiable contributing factors, many of which are driven by how we think or related to the external environment. These contributing factors lead to both perceptual and interpretive errors. Identifying contributing factors is one of the keys to developing interventions that reduce or mitigate diagnostic errors. Developing a comprehensive process to identify diagnostic errors, analyze them to discover contributing factors and biases, and develop interventions based on the contributing factors is fundamental to learning from diagnostic error. Coupled with effective peer learning practices, supportive leadership, and a culture of quality, this process can unquestionably result in fewer diagnostic errors, improved patient outcomes, and increased satisfaction for all stakeholders. This article provides the foundational elements for implementing this type of process at a radiology practice, with examples to help radiologists and practice leaders achieve meaningful practice improvement. ©RSNA, 2018.

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Year:  2018        PMID: 30303801     DOI: 10.1148/rg.2018180021

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


  9 in total

1.  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

2.  Gray scale inversion imaging (GSI) in Trauma and Orthopaedics.

Authors:  A Shah; K P Iyengar; R Botchu
Journal:  J Orthop       Date:  2022-02-16

3.  Accuracy and Clinical Utility of Reports from Outside Hospitals for CT of the Cervical Spine in Blunt Trauma.

Authors:  K Rao; J M Engelbart; J Yanik; J Hall; S Swenson; B Policeni; J Maley; C Galet; T Granchi; D A Skeete
Journal:  AJNR Am J Neuroradiol       Date:  2021-11-04       Impact factor: 3.825

4.  Diagnostic Errors in Cerebrovascular Pathology: Retrospective Analysis of a Neuroradiology Database at a Large Tertiary Academic Medical Center.

Authors:  G Biddle; R Assadsangabi; K Broadhead; L Hacein-Bey; V Ivanovic
Journal:  AJNR Am J Neuroradiol       Date:  2022-08-04       Impact factor: 4.966

5.  Exercise in Clinical Reasoning: Trust but Verify.

Authors:  Kerry Scott Griffin; Lindsey C Shipley; Sanjiv Bajaj; Robert M Centor
Journal:  J Gen Intern Med       Date:  2022-08-24       Impact factor: 6.473

6.  Analyzing diagnostic errors in the acute setting: a process-driven approach.

Authors:  Jacqueline A Griffin; Kevin Carr; Kerrin Bersani; Nicholas Piniella; Daniel Motta-Calderon; Maria Malik; Alison Garber; Kumiko Schnock; Ronen Rozenblum; David W Bates; Jeffrey L Schnipper; Anuj K Dalal
Journal:  Diagnosis (Berl)       Date:  2021-08-23

Review 7.  Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review.

Authors:  Omer Onder; Yasin Yarasir; Aynur Azizova; Gamze Durhan; Mehmet Ruhi Onur; Orhan Macit Ariyurek
Journal:  Insights Imaging       Date:  2021-04-20

8.  Automatic text classification of actionable radiology reports of tinnitus patients using bidirectional encoder representations from transformer (BERT) and in-domain pre-training (IDPT).

Authors:  Jia Li; Yucong Lin; Pengfei Zhao; Wenjuan Liu; Linkun Cai; Jing Sun; Lei Zhao; Zhenghan Yang; Hong Song; Han Lv; Zhenchang Wang
Journal:  BMC Med Inform Decis Mak       Date:  2022-07-30       Impact factor: 3.298

9.  Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data.

Authors:  Joceline Ziegler; Bjarne Pfitzner; Heinrich Schulz; Axel Saalbach; Bert Arnrich
Journal:  Sensors (Basel)       Date:  2022-07-11       Impact factor: 3.847

  9 in total

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