Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Journal: Critical care medicine

PMID: 31135500

Abstract

OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).

Authors

Jennifer C Ginestra

Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
Heather M Giannini

Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA.
William D Schweickert

University of Pennsylvania Health System, Philadelphia, PA.
Laurie Meadows

Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
Michael J Lynch

Department of Nursing, Hospital of the University of Pennsylvania, Philadelphia, PA.
Kimberly Pavan

Penn Presbyterian Medical Center, Philadelphia, PA.
Corey J Chivers

University of Pennsylvania Health System, Philadelphia, PA.
Michael Draugelis

University of Pennsylvania Health System, Philadelphia, PA.
Patrick J Donnelly

Department of Electrical Engineering and Computer Science, Science, Oregon State University Cascades Campus, Bend, OR, United States.
Barry D Fuchs

1 Pulmonary, Allergy, and Critical Care Division, Hospital of the University of Pennsylvania.
Craig A Umscheid

University of Pennsylvania Health System, Philadelphia, PA.

Keywords

Algorithms Attitude of Health Personnel Decision Support Systems, Clinical Diagnosis, Computer-Assisted Electronic Health Records Hospitals, Teaching Humans Machine Learning Medical Staff, Hospital Nursing Staff, Hospital Practice Patterns, Nurses' Practice Patterns, Physicians' Prospective Studies Sepsis Shock, Septic Text Messaging

External Resources

View on PubMed Access via DOI PubMed (31135500)

Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Abstract

Authors

Keywords

External Resources

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