A novel method to assess incompleteness of mammography reports

AMIA Annu Symp Proc. 2014 Nov 14;2014:1758-67. eCollection 2014.


Mammography has been shown to improve outcomes of women with breast cancer, but it is subject to inter-reader variability. One well-documented source of such variability is in the content of mammography reports. The mammography report is of crucial importance, since it documents the radiologist's imaging observations, interpretation of those observations in terms of likelihood of malignancy, and suggested patient management. In this paper, we define an incompleteness score to measure how incomplete the information content is in the mammography report and provide an algorithm to calculate this metric. We then show that the incompleteness score can be used to predict errors in interpretation. This method has 82.6% accuracy at predicting errors in interpretation and can possibly reduce total diagnostic errors by up to 21.7%. Such a method can easily be modified to suit other domains that depend on quality reporting.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Bayes Theorem
  • Breast Neoplasms / diagnostic imaging*
  • Diagnostic Errors
  • Female
  • Humans
  • Mammography*
  • Monte Carlo Method
  • Predictive Value of Tests