Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic

Andrew Wong; Jie Cao; Patrick G Lyons; Sayon Dutta; Vincent J Major; Erkin Ötles; Karandeep Singh

doi:10.1001/jamanetworkopen.2021.35286

Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic

JAMA Netw Open. 2021 Nov 1;4(11):e2135286. doi: 10.1001/jamanetworkopen.2021.35286.

Authors

Andrew Wong¹, Jie Cao², Patrick G Lyons³, Sayon Dutta⁴, Vincent J Major⁵, Erkin Ötles^{6

7}, Karandeep Singh^{1

8

9}

Affiliations

¹ Department of Internal Medicine, University of Michigan Medical School, Ann Arbor.
² Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor.
³ John T. Milliken Department of Medicine, Washington University School of Medicine, St Louis, Missouri.
⁴ Department of Emergency Medicine, Massachusetts General Hospital, Boston.
⁵ Department of Population Health, New York University Grossman School of Medicine, New York.
⁶ Medical Scientist Training Program, University of Michigan, Ann Arbor.
⁷ Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor.
⁸ Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor.
⁹ School of Information, University of Michigan, Ann Arbor.

Abstract

This descriptive study evaluates the association between nursing reports of sepsis overalerting and alert volume by quantifying the number of alerts generated by the Epic Sepsis Model at 24 US hospitals before and during the COVID-19 pandemic.

Publication types

Multicenter Study
Research Support, N.I.H., Extramural

MeSH terms

COVID-19*
Decision Support Systems, Clinical / statistics & numerical data*
Emergency Service, Hospital / statistics & numerical data*
Hospitals
Humans
Population Surveillance*
Retrospective Studies
SARS-CoV-2
Sepsis / diagnosis*
United States / epidemiology

Abstract

Publication types

MeSH terms

Grants and funding