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Area of Science:

  • Radiology and Medical Informatics
  • Natural Language Processing in Healthcare
  • Machine Learning Applications in Medicine

Background:

  • Inter-radiologist variability in reporting can affect patient care and diagnostic accuracy.
  • The increasing use of electronic health records necessitates standardized and reliable reporting methods.

Purpose of the Study:

  • To develop and apply a natural language processing and machine learning algorithm to quantify inter-radiologist report variation.
  • To compare reporting variation between radiologists using structured versus free-text reporting formats.

Main Methods:

  • Analysis of 28,615 radiology reports using metrics like verbosity, observational terms, unwarranted negatives, and repeated language.
  • Comparison of reports from templated ultrasound (appendicitis) and free-text chest radiograph studies.
  • Statistical analysis including Wilcoxon rank tests to compare metrics and identify outlier reports.

Main Results:

  • Significant inter-radiologist variability in dictation styles was observed, with outlier metrics varying up to tenfold.
  • Reports using free text exhibited significantly higher metric values (P < 0.0001) compared to more structured reports.
  • The developed algorithm successfully identified reporting profile variations, especially in free-text reports.

Conclusions:

  • The algorithm effectively quantifies radiologist reporting variability, highlighting differences between structured and free-text approaches.
  • Variability in radiology reports, particularly those with free text, poses a challenge to effective communication and reliable patient care.