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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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Published in:Journal of Digital Imaging Vol. 23; no. 2; pp. 109 - 119
Main Author: Reiner B
Format: tables/charts Journal Article
Published: Springer Nature Apr2010
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: Uncovering and improving upon the inherent deficiencies of radiology reporting through data mining.
      aug:
        au: Reiner B
        affil: Department of Radiology, Maryland VA Healthcare System, 10 North Greene Street Baltimore 21201 USA
      sug:
        subj:
          Data Mining
          Diagnostic Imaging
          Reports
          Artificial Intelligence
          Decision Making, Clinical
          Knowledge Bases
          Report Writing Methods
      ab: 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.
      pubtype: Academic Journal
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        Journal Article
      ougenre: Article
    language: English
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