Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

Journal: Journal of medical Internet research
PMID: 39094106

Abstract

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.

Authors

  • Amir Kamel Rahimi
    Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Oliver Pienaar
    The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia.
  • Moji Ghadimi
    The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia.
  • Oliver J Canfell
    Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Jason D Pole
    Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Sally Shrapnel
    Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia.
  • Anton H van der Vegt
    The University of Queensland, St Lucia, Qld, Australia.
  • Clair Sullivan
    Queensland Digital Health Centre, University of Queensland, Brisbane, QLD.

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