Comparison Between Manual Auditing and a Natural Language Process With Machine Learning Algorithm to Evaluate Faculty Use of Standardized Reports in Radiology.

Journal: Journal of the American College of Radiology : JACR
Published Date: Dec 18, 2017

Abstract

PURPOSE: When implementing or monitoring department-sanctioned standardized radiology reports, feedback about individual faculty performance has been shown to be a useful driver of faculty compliance. Most commonly, these data are derived from manual audit, which can be both time-consuming and subject to sampling error. The purpose of this study was to evaluate whether a software program using natural language processing and machine learning could accurately audit radiologist compliance with the use of standardized reports compared with performed manual audits.

Authors

  • Carolina V Guimaraes
    Department of Radiology, Texas Children's Hospital, Houston, Texas; Department of Radiology, Stanford University, Stanford, California.
  • Robert Grzeszczuk
    InContext, Houston, Texas.
  • George S Bisset
    Department of Radiology, Texas Children's Hospital, Houston, Texas.
  • Lane F Donnelly
    Department of Radiology, Texas Children's Hospital, Houston, Texas; Department of Radiology, Stanford University, Stanford, California. Electronic address: lane.donnelly@stanford.edu.

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