Work

A novel method to assess incompleteness of mammography reports.

Year: 2014

Type: conference-paper

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.

Source: PubMed

Authors: Francisco J Gimenez, Yirong Wu, Elizabeth S Burnside, Daniel L Rubin

Institutions: Stanford University, University of Wisconsin–Madison, Stanford Medicine

Language: English

Topic: Radiology practices and education

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Cancer Institute, U.S. National Library of Medicine

Awards: Computerized Quantitative Imaging Assessment of Tumor Burden, A Real-Time Feedback System For Radiological Reporting To Reduce Diagnostic Varia, Integrating Machine Learning and Physician Expertise for Breast Cancer Diagnosis, Machine Learning for Improved Mammography Screening, Biomedical Informatics Training Program at Stanford