Nuclear cardiology reporting: Leaving an impression

Nuclear cardiology reporting: Leaving an impression EDITORIAL Nuclear cardiology reporting: Leaving an impression a a Edwin Wu, MD, and Thomas A. Holly, MD Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL Received May 20, 2018; accepted May 21, 2018 doi:10.1007/s12350-018-1315-7 range from cumbersome to burdensome, and despite the See related article, https://doi.org/10. potential benefits, physicians frequently cite the onerous 1007/s12350-018-1275-y. work necessary to utilize any form of structure within their reports. In this issue of the Journal, Levy et al. explore the The performance and interpretation of myocardial possible use of Natural Language Processing (NLP) to perfusion imaging studies are obviously vital compo- determine ischemic risk assessment from stress nents of the stress testing process. How one reports the myocardial perfusion imaging reports. The authors findings to the referring doctor and other healthcare propose that NLP could be used to improve physicians’ providers is an all too important, and often neglected, understanding of these results and improve utilization, part of the performance of any diagnostic test. Medical specifically, proper utilization of cardiac catheterization testing is frequently not binomial (normal/abnormal), and revascularization. However, before NLP automated but rather provides a mixture of current and future car- algorithms can be employed to assist with http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Nuclear Cardiology Springer Journals

Nuclear cardiology reporting: Leaving an impression

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Publisher
Springer US
Copyright
Copyright © 2018 by American Society of Nuclear Cardiology
Subject
Medicine & Public Health; Cardiology; Nuclear Medicine; Imaging / Radiology
ISSN
1071-3581
eISSN
1532-6551
D.O.I.
10.1007/s12350-018-1315-7
Publisher site
See Article on Publisher Site

Abstract

EDITORIAL Nuclear cardiology reporting: Leaving an impression a a Edwin Wu, MD, and Thomas A. Holly, MD Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL Received May 20, 2018; accepted May 21, 2018 doi:10.1007/s12350-018-1315-7 range from cumbersome to burdensome, and despite the See related article, https://doi.org/10. potential benefits, physicians frequently cite the onerous 1007/s12350-018-1275-y. work necessary to utilize any form of structure within their reports. In this issue of the Journal, Levy et al. explore the The performance and interpretation of myocardial possible use of Natural Language Processing (NLP) to perfusion imaging studies are obviously vital compo- determine ischemic risk assessment from stress nents of the stress testing process. How one reports the myocardial perfusion imaging reports. The authors findings to the referring doctor and other healthcare propose that NLP could be used to improve physicians’ providers is an all too important, and often neglected, understanding of these results and improve utilization, part of the performance of any diagnostic test. Medical specifically, proper utilization of cardiac catheterization testing is frequently not binomial (normal/abnormal), and revascularization. However, before NLP automated but rather provides a mixture of current and future car- algorithms can be employed to assist with

Journal

Journal of Nuclear CardiologySpringer Journals

Published: Jun 5, 2018

References

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