Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.

Journal: Critical care explorations
Published Date: Jun 4, 2025

Abstract

IMPORTANCE: Sepsis is a major cause of morbidity and mortality, with early intervention shown to improve outcomes. Predictive modeling and artificial intelligence (AI) can aid in early sepsis recognition, but there remains a gap between algorithm development and clinical use. Despite the importance of user experience in adopting clinical predictive models, few studies have focused on provider acceptance and feedback.

Authors

  • Karthik Ramesh
    School of Medicine, University of California San Diego, San Diego, CA 92093, United States.
  • Aaron Boussina
    Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
  • Supreeth P Shashikumar
  • Atul Malhotra
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California, San Diego, La Jolla, CA. Electronic address: amalhotra@health.ucsd.edu.
  • Christopher A Longhurst
    Department of Medicine, University of California San Diego, La Jolla, California, USA clonghurst@health.ucsd.edu.
  • Christopher S Josef
    Department of Surgery, Emory University School of Medicine, Atlanta, USA.
  • Kimberly Quintero
    Department of Quality, University of California San Diego, San Diego, CA.
  • Jake Del Rosso
    Department of Emergency Medicine, Cottage Hospital, Santa Barbara, CA.
  • Shamim Nemati
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
  • Gabriel Wardi
    Emergency Medicine, University of California San Diego, La Jolla, California, USA.

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