A survey of extant organizational and computational setups for deploying predictive models in health systems.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Oct 12, 2021

Abstract

OBJECTIVE: Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.

Authors

  • Sehj Kashyap
    Duke Institute for Health Innovation, Durham, North Carolina, United States of America.
  • Keith E Morse
    Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
  • Birju Patel
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

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