The Effect of Faster Reporting Speed for Imaging Studies on the Number of Misses and Interpretation Errors: A Pilot Study

Sokolovskaya, Evgeniya; Shinde, Tejas; Ruchman, Richard B.; Kwak, Andrew; Lu, Shi‐En; Shariff, Yasmeen K.; Wiggins, Ernest F.; Talangbayan, Leizle

doi:10.1016/j.jacr.2015.03.040

Cited by 86 publications

(48 citation statements)

References 11 publications

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“…That would translate to a productivity of no more than 6-12 reports per 6-h shift. In contrast, other studies have reported that that the mean time for writing the report of a CT examination of the abdomen and pelvis is 10-15 min, which would translate to a productivity of 24-36 reports per 6-h shift (10) .…”

Section: Discussionmentioning

confidence: 81%

“…Although productivity-based pay encourages professionals to issue more reports, it can lead to work overload and more diagnostic errors. Studies confirm that an increase in the number of reports issued per shift affects the interpretive accuracy and increases the error rate in those reports (10,11) . Sokolovskaya et al (10) studied radiologists who were asked to interpret imaging examinations at twice their usual speed and found that the number of significant errors increased from 10.0% to 26.6% (10) .…”

Section: Discussionmentioning

confidence: 89%

“…Studies confirm that an increase in the number of reports issued per shift affects the interpretive accuracy and increases the error rate in those reports (10,11) . Sokolovskaya et al (10) studied radiologists who were asked to interpret imaging examinations at twice their usual speed and found that the number of significant errors increased from 10.0% to 26.6% (10) . Other studies have also shown that long workdays with a large volume of examinations to be read reduce the diagnostic accuracy of radiologists (12,13) .…”

Section: Discussionmentioning

confidence: 89%

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Estimating the productivity of radiologists in Brazil: the search for a benchmark

et al. 2020

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Objective: To determine the average productivity of radiologists, as measured by number of reports issued per 6-h shift, evaluating variables that could affect the results. Materials and Methods: This was a study utilizing an online questionnaire sent to radiologists affiliated with the Brazilian College of Radiology and Diagnostic Imaging. The questions were related to the demographic profile and professional practice characteristics (form of remuneration, primary imaging method employed, and subspecialty) of the radiologists, as well as their individual productivity (average personal productivity) and the productivity considered reasonable in a 6-h shift. The association between productivity and the practice characteristics of the radiologists was determined by using Poisson regression to calculate the prevalence ratio. Results: A total of 510 radiologists completed the questionnaire. The great majority of the respondents (84%) reported that their remuneration is directly related to their productivity. The productivity varied according to the subspecialty, work environment, and remuneration model. Conclusion: We demonstrated that the productivity of radiologists is associated with the characteristics of their employment. We hope that this study will encourage other studies aimed at evaluating the productive capacity of the radiologists in Brazil, addressing the various functions they perform in their daily routine, including activities other than issuing reports.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 89%

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Estimating the productivity of radiologists in Brazil: the search for a benchmark

et al. 2020

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“…However, manually annotating a large dataset to fulfill the data-hungry deep learning models is timeconsuming and expensive. For example, a radiologist usually read at a speed of 10 mins per example for CT scan studies [1]. Instead, researchers may benefit from using textmining to generate annotations even if those annotations are of modest accuracy [2].…”

Section: Related Workmentioning

confidence: 99%

A self-attention based deep learning method for lesion attribute detection from CT reports

Peng

Yan

Sandfort

et al. 2019

2019 IEEE International Conference on Healthcare Informatics (ICHI)

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In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity. While these lesion attributes are rich and useful in many downstream clinical applications, how to extract them from the radiology reports is less studied. This paper outlines a novel deep learning method to automatically extract attributes of lesions of interest from the clinical text. Different from classical CNN models, we integrated the multi-head self-attention mechanism to handle the long-distance information in the sentence, and to jointly correlate different portions of sentence representation subspaces in parallel. Evaluation on an in-house corpus demonstrates that our method can achieve high performance with 0.848 in precision, 0.788 in recall, and 0.815 in F-score. The new method and constructed corpus will enable us to build automatic systems with a higher-level understanding of the radiological world.Index Terms-deep learning, lesion attribute detection, CNN

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“…Finally, it is thought that an increased incidence of perceptual errors may be attributable to certain human risk factors such as increased pace of interpretation, fatigue, and work-place distractions such as phone calls and e-mails [3,27,28]. The average radiologist work-load has increased dramatically.…”

Section: Contributing Factors To Perceptual Errormentioning

confidence: 99%

Perceptual errors in pediatric radiology

Taylor

2017

Diagnosis

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Perceptual errors are common contributors to missed diagnoses in the clinical practice of radiology. While the physical attributes of an image such as image resolution, signal-to-noise characteristics, and anatomic complexity are major causes of poor conspicuity of pathologic lesions, there are major interrelated cognitive contributors to visual errors. The first is satisfaction of search (SOS), where the detection of an abnormality results in premature termination of further search. Another form of incomplete search pattern is visual isolation, where a radiologist's search pattern is truncated to the main areas of an image, while little or no attention is given to peripheral areas. A second cognitive error is inattentional blindness, defined as the failure to notice a fully visible, but unexpected object because attention was otherwise engaged. Strategies for error mitigation have centered around the use of check lists, self prompting routines, and structured reporting within an institutional culture of safety and vigilance.

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The Effect of Faster Reporting Speed for Imaging Studies on the Number of Misses and Interpretation Errors: A Pilot Study

Cited by 86 publications

References 11 publications

Estimating the productivity of radiologists in Brazil: the search for a benchmark

Estimating the productivity of radiologists in Brazil: the search for a benchmark

A self-attention based deep learning method for lesion attribute detection from CT reports

Perceptual errors in pediatric radiology

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