Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39401245

Abstract

OBJECTIVES: Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assessed general public and surgical patient attitudes and perspectives on ML-CDSS use in perioperative care.

Authors

  • Xiomara T Gonzalez
    Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Karen Steger-May
    Center for Biostatistics and Data Science, Washington University School of Medicine, St Louis, MO 63110, United States.
  • Joanna Abraham
    Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.

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