Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

Journal: JAMA internal medicine
PMID: 38526472

Abstract

IMPORTANCE: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioration, but there is limited evidence for their effectiveness.

Authors

  • Robert J Gallo
    Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA, USA.
  • Lisa Shieh
    Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
  • Margaret Smith
    Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA 94305, United States.
  • Ben J Marafino
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Pascal Geldsetzer
    Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.
  • Steven M Asch
    Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA; Division of General Medical Disciplines, Stanford University, Stanford, CA.
  • Kenny Shum
    Department of Medicine, Stanford University, Stanford, California.
  • Steven Lin
    Stanford University School of Medicine.
  • Jerri Westphal
    Department of Medicine, Stanford University, Stanford, California.
  • Grace Hong
    Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • Ron Chen Li
    Department of Medicine, Stanford University, Stanford, California.

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