Achieving large-scale clinician adoption of AI-enabled decision support.

Journal: BMJ health & care informatics
Published Date: May 30, 2024

Abstract

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

Authors

  • Ian A Scott
    University of Queensland, Brisbane, Queensland, Australia (I.A.S.).
  • Anton van der Vegt
    Centre for Health Services Research, University of Queensland, Brisbane, QLD.
  • Paul Lane
    Safety, Quality and Innovation, The Prince Charles Hospital, Brisbane, Queensland, Australia.
  • Steven McPhail
    Australian Centre for Health Services Innovation, Queensland University of Technology Faculty of Health, Brisbane, Queensland, Australia.
  • Farah Magrabi
    Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Australia.

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