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Area of Science:

  • Radiology
  • Medical Imaging Analysis
  • Machine Learning in Healthcare

Background:

  • Variation in radiologist follow-up imaging recommendations is unknown.
  • Identifying factors for variation may prevent unnecessary tests for incidental findings.

Purpose of the Study:

  • Determine incidence of follow-up recommendations.
  • Identify factors associated with these recommendations across modalities and settings.

Main Methods:

  • Retrospective analysis of 318,366 radiology reports.
  • Machine learning algorithm trained to predict follow-up recommendations.
  • Multivariable logistic regression and subspecialty division analysis.

Main Results:

  • 12.2% of reports included follow-up recommendations.
  • Older patients and CT studies had higher recommendation rates.
  • No significant association found with radiologist sex, trainee presence, or years in practice.
  • Significant interradiologist variation (2.8- to 6.7-fold) observed.

Conclusions:

  • Substantial interradiologist variation exists in follow-up recommendation probability.
  • Variation persists after adjusting for patient, examination, and radiologist factors.