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Search results for "Information Professionals"
Panner M. Forbes. August 12, 2019.
Diagnostic errors can result in harm across the spectrum of practice. Discussing cognitive and system factors in radiology that contribute to diagnostic mistakes, this magazine article recommends ways to reduce risk of errors, including peer review of practice, structured reporting, and artificial intelligence–enabled decision support.
Journal Article > Study
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.
Ardila D, Kiraly AP, Bharadwaj S, et al. Nat Med. 2019;25:954-961.
Researchers developed a deep learning algorithm to predict a patient's risk of lung cancer using information from current and prior CT scans. They found that the model performed better than six radiologists when prior imaging for the patient was available, with a reduction in both false positives and false negatives. The authors conclude that the use of such algorithms represents an opportunity to improve lung cancer screening processes.
Bruno MA. New York, NY: Oxford University Press; 2019. ISBN: 9780190665395.
Despite enhancements in medical imaging technology, diagnostic radiologists are still susceptible to uncertainty, bias, and overconfidence that hinder accurate image assessment. Discussing the scope and impact of human error in diagnostic radiology, this book explores the future of advanced information technologies in diagnostic radiology and provides recommendations to reduce the effect of human fallibility on imaging interpretation.
Journal Article > Commentary
Degnan AJ, Ghobadi EH, Hardy P, et al. Acad Radiol. 2019;26:833-845.
Human limitations can affect the safety of practice. This article discusses how fatigue, information quality, distractions, and the physical environment influence radiologic interpretation. The authors highlight the role of artificial intelligence as a potential solution to reduce errors in interpreting diagnostic imaging.
Journal Article > Study
Assessing information sources to elucidate diagnostic process errors in radiologic imaging—a human factors framework.
Cochon L, Lacson R, Wang A, et al. J Am Med Inform Assoc. 2018;25:1507-1515.
As the diagnostic safety field has matured, researchers are striving to better define the diagnostic process and identify failure modes that may lead to patient harm. This study utilized human factors engineering approaches to characterize the information sources used in radiologic diagnostic imaging according to the Systems Engineering Initiative for Patient Safety (SEIPS) framework. Most potential errors were related to person-related factors, such as inadequate communication between clinicians, rather than technological factors.
Journal Article > Review
Massalha S, Clarkin O, Thornhill R, Wells G, Chow BJW. Can J Cardiol. 2018;34:827-838.
Decision support tools can help reduce diagnostic uncertainty. Discussing how artificial intelligence can be utilized to inform diagnostic decision making and improve the accuracy of cardiac image interpretation, this review suggests that use of such technology can reduce production pressure and cognitive load for imaging physicians.