Perceptual and Interpretive Error in Diagnostic Radiology—Causes and Potential Solutions

Degnan, Andrew J.; Ghobadi, Emily H.; Hardy, Peter; Krupiński, Elizabeth A.; Scali, Elena P.; Stratchko, Lindsay; Ulano, Adam; Walker, E. Ronald; Wasnik, Ashish P.; Auffermann, William F.

doi:10.1016/j.acra.2018.11.006

Cited by 70 publications

(64 citation statements)

References 90 publications

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“…Funaki et al (1997) and Rosenkrantz and Bansal (2016) found that missed findings accounted for 60–80% of interpretive error. Thus, faulty detection—failure to identify salient findings—is considered the most important source of interpretive error in radiology (Degnan et al, 2018).…”

Section: Error Rates In Radiologymentioning

confidence: 99%

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Waite

Grigorian

Alexander

et al. 2019

Front. Hum. Neurosci.

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Radiologists rely principally on visual inspection to detect, describe, and classify findings in medical images. As most interpretive errors in radiology are perceptual in nature, understanding the path to radiologic expertise during image analysis is essential to educate future generations of radiologists. We review the perceptual tasks and challenges in radiologic diagnosis, discuss models of radiologic image perception, consider the application of perceptual learning methods in medical training, and suggest a new approach to understanding perceptional expertise. Specific principled enhancements to educational practices in radiology promise to deepen perceptual expertise among radiologists with the goal of improving training and reducing medical error.

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Section: Error Rates In Radiologymentioning

confidence: 99%

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Waite

Grigorian

Alexander

et al. 2019

Front. Hum. Neurosci.

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“…Satisfaction of search (sometimes referred to as subsequent search miss (SSM) error) is a recognised source of radiological error that suggests a reviewer may miss subsequent pathology, once an initial pathological target has been identified. Research into this concept has roots in academic radiology and psychology with proposed contributors including the various and complicated components of visual search, interruptions and fatigue . It is suggested that a visual search is more difficult in the presence of multiple targets in the same search display, a trauma radiograph for example.…”

Section: Methodsmentioning

confidence: 99%

“…Resource depletion, another factor, refers to the reviewer having insufficient working memory to process subsequent targets due to subconscious decision‐making in relation to the primary finding . It has also been suggested that the nature of the first pathology may bias a reviewer to look for other targets similar to the first, thereby overlooking targets of a different nature …”

Section: Methodsmentioning

confidence: 99%

Communicating traumatic pathology to ensure shared understanding: is there a recipe for the perfect preliminary image evaluation?

Cooper

Neep

Eastgate

2020

J of Medical Radiation Sci

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Medical imaging and emergency departments work collaboratively to interpret trauma radiographs. In addition to accurate radiographic interpretation, clear communication is crucial to ensure appropriate and timely management of musculoskeletal injuries. This two-step 'how to guide' provides the reviewer with a recipe for effectively evaluating trauma radiographs for traumatic pathology and succinctly documenting the findings.Step 1 is a systematic search of the radiograph: soft tissues, bones, alignment of joints and satisfaction of search (SBASS). Utilising SBASS increases reviewer confidence in identifying traumatic pathology of the appendicular and axial skeleton.Step 2 is a streamlined communication model for the documentation of pathological findings. The WWW acronym (What is it? Where is it? What is it doing?) can be adapted to describe simple or complex traumatic pathology.

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“…3 Error has been studied in depth in radiology for >60 years and technology has advanced considerably, yet fundamental error rates have not changed significantly here or in most other clinical specialties. [4][5][6][7][8][9][10][11][12][13][14] Why? Humans make errors.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence: Lessons Learned from Radiology

Krupiński

2019

Healthcare Transformation

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Artificial intelligence (AI) will continue to increase its significant impact on the everyday acquisition, interpretation, and application of data in our healthcare system. It is difficult to predict whether and to what extent AI will change the health status of patients, but it certainly will change the way decisions are made, how healthcare is delivered, and the ways providers, patients, and healthcare enterprises interact with and use data from an increasing array of sources. Radiology has often been at the forefront of technological change in healthcare and the AI revolution is no different. The lessons radiology has learned to date, its successes and challenges, can help provide guidance for other specialties, especially since radiology interacts with and cross-cuts nearly every other specialty and touches nearly every patient at least once in his or her lifetime.

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Perceptual and Interpretive Error in Diagnostic Radiology—Causes and Potential Solutions

Cited by 70 publications

References 90 publications

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Analysis of Perceptual Expertise in Radiology – Current Knowledge and a New Perspective

Communicating traumatic pathology to ensure shared understanding: is there a recipe for the perfect preliminary image evaluation?

Artificial Intelligence: Lessons Learned from Radiology

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