Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

Journal: Critical care medicine
PMID: 38380992

Abstract

OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations.

Authors

  • Matthew A Levin
    Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA.
  • Arash Kia
    Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.
  • Prem Timsina
    Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
  • Fu-Yuan Cheng
    Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Kim-Anh-Nhi Nguyen
    Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Roopa Kohli-Seth
    Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Hung-Mo Lin
    Department of Anesthesiology and Yale Center for Analytical Sciences, Yale School of Medicine, New Haven, CT.
  • Yuxia Ouyang
    Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Robert Freeman
    Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Pl, New York, NY 10029, USA.
  • David L Reich
    Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

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