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Quantitative Analysis of Uncertainty in Medical Reporting: Creating a Standardized and Objective Methodology.

Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizi...

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Published in:Journal of Digital Imaging Vol. 31; no. 2; pp. 145 - 150
Main Author: Reiner, Bruce I.
Format: tables/charts Journal Article
Published: Springer Nature Apr2018
Online Access:View this record in EBSCOhost
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      pub: Springer Nature
      place: New York, New York
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        atl: Quantitative Analysis of Uncertainty in Medical Reporting: Creating a Standardized and Objective Methodology.
      aug:
        au: Reiner, Bruce I.
        affil: Department of Radiology, Veterans Affairs Maryland Healthcare System, 10 North Greene Street, Baltimore, MD 21201, USA
      sug:
        subj:
          Uncertainty
          Reports
          Medical Records
          Quantitative Studies
          Text Messaging
          Validity
          Confidence
          Natural Language Processing
          Machine Learning
          Decision Support Systems, Clinical
      ab: Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogramanalysis, topic modeling, and machine learning. The derived uncertainty data offers the potential to objectively analyze report uncertainty in real time and correlate with outcomes analysis for the purpose of context and user-specific decision support at the point of care, where intervention would have the greatest clinical impact.
      pubtype: Academic Journal
      doctype:
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        Journal Article
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    language: English
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