Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study.

Journal: Journal of medical Internet research
Published Date: Nov 19, 2020

Abstract

BACKGROUND: Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care.

Authors

  • Sahil Sandhu
    Trinity College of Arts & Sciences, Duke University, Durham, NC, United States.
  • Anthony L Lin
    Duke University School of Medicine, Durham, NC, United States.
  • Nathan Brajer
    Duke Institute for Health Innovation, Durham, North Carolina.
  • Jessica Sperling
    Duke University Social Science Research Institute, Durham, NC.
  • William Ratliff
    Duke Institute for Health Innovation, Durham, NC, United States.
  • Armando D Bedoya
    Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, United States.
  • Suresh Balu
    Duke Institute for Health Innovation.
  • Cara O'Brien
    Department of Medicine, Duke University School of Medicine, Durham, NC, United States.
  • Mark P Sendak
    Duke Institute for Health Innovation, Durham, NC, United States.

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