title: A novel method to assess incompleteness of mammography reports.
abstract: 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.
authors.affiliation: Biomedical Informatics Training Program, Stanford, CA.
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Gimenez
authors.affiliation: Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI.
authors.forename: Yirong
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
authors.affiliation: Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003951:Diagnostic Errors; D005260:Female; D006801:Humans; D008327:Mammography; D009010:Monte Carlo Method; D011237:Predictive Value of Tests