The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network.

Journal: Journal of the American College of Radiology : JACR
Published Date: Sep 1, 2019

Abstract

Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and stakeholders in the radiology value network may be mitigated though standardization, automation, and a focus on workflow efficiency. In this article the authors present applications of these various strategies via use cases for quality improvement projects at different points in the radiology value network. In addition, the authors discuss opportunities for machine-learning applications in data aggregation as opposed to traditional applications in data extraction.

Authors

  • Valeria Makeeva
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: valeria.makeeva@emory.edu.
  • Judy Gichoya
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • C Matthew Hawkins
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.
  • Alexander J Towbin
    Department of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
  • Marta Heilbrun
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.
  • Adam Prater
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).

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