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Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining
Uncertainty has been the perceived Achilles heel of the radiology report since the inception of the free-text report. As a measure of diagnostic confidence (or lack thereof), uncertainty in reporting has the potential to lead to diagnostic errors, delayed clinical decision making, increased cost of...
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Formato: | Texto |
Lenguaje: | English |
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Springer-Verlag
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837185/ https://www.ncbi.nlm.nih.gov/pubmed/20162438 http://dx.doi.org/10.1007/s10278-010-9279-4 |
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author | Reiner, Bruce |
author_facet | Reiner, Bruce |
author_sort | Reiner, Bruce |
collection | PubMed |
description | Uncertainty has been the perceived Achilles heel of the radiology report since the inception of the free-text report. As a measure of diagnostic confidence (or lack thereof), uncertainty in reporting has the potential to lead to diagnostic errors, delayed clinical decision making, increased cost of healthcare delivery, and adverse outcomes. Recent developments in data mining technologies, such as natural language processing (NLP), have provided the medical informatics community with an opportunity to quantify report concepts, such as uncertainty. The challenge ahead lies in taking the next step from quantification to understanding, which requires combining standardized report content, data mining, and artificial intelligence; thereby creating Knowledge Discovery Databases (KDD). The development of this database technology will expand our ability to record, track, and analyze report data, along with the potential to create data-driven and automated decision support technologies at the point of care. For the radiologist community, this could improve report content through an objective and thorough understanding of uncertainty, identifying its causative factors, and providing data-driven analysis for enhanced diagnosis and clinical outcomes. |
format | Text |
id | pubmed-2837185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Springer-Verlag |
record_format | MEDLINE/PubMed |
spelling | pubmed-28371852010-03-24 Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining Reiner, Bruce J Digit Imaging Article Uncertainty has been the perceived Achilles heel of the radiology report since the inception of the free-text report. As a measure of diagnostic confidence (or lack thereof), uncertainty in reporting has the potential to lead to diagnostic errors, delayed clinical decision making, increased cost of healthcare delivery, and adverse outcomes. Recent developments in data mining technologies, such as natural language processing (NLP), have provided the medical informatics community with an opportunity to quantify report concepts, such as uncertainty. The challenge ahead lies in taking the next step from quantification to understanding, which requires combining standardized report content, data mining, and artificial intelligence; thereby creating Knowledge Discovery Databases (KDD). The development of this database technology will expand our ability to record, track, and analyze report data, along with the potential to create data-driven and automated decision support technologies at the point of care. For the radiologist community, this could improve report content through an objective and thorough understanding of uncertainty, identifying its causative factors, and providing data-driven analysis for enhanced diagnosis and clinical outcomes. Springer-Verlag 2010-02-17 2010-04 /pmc/articles/PMC2837185/ /pubmed/20162438 http://dx.doi.org/10.1007/s10278-010-9279-4 Text en © Society for Imaging Informatics in Medicine 2010 |
spellingShingle | Article Reiner, Bruce Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining |
title | Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining |
title_full | Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining |
title_fullStr | Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining |
title_full_unstemmed | Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining |
title_short | Uncovering and Improving Upon the Inherent Deficiencies of Radiology Reporting through Data Mining |
title_sort | uncovering and improving upon the inherent deficiencies of radiology reporting through data mining |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837185/ https://www.ncbi.nlm.nih.gov/pubmed/20162438 http://dx.doi.org/10.1007/s10278-010-9279-4 |
work_keys_str_mv | AT reinerbruce uncoveringandimprovingupontheinherentdeficienciesofradiologyreportingthroughdatamining |