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

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Article Information

Accepted for Publication: September 26, 2021.

Published: November 19, 2021. doi:10.1001/jamanetworkopen.2021.35286

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Wong A et al. JAMA Network Open.

Corresponding Author: Karandeep Singh, MD, MMSc, Department of Learning Health Sciences, University of Michigan Medical School, 1161H NIB, 300 N Ingalls St, Ann Arbor, MI 48109 (kdpsingh@umich.edu).

Author Contributions: Dr Singh had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Dr Wong and Ms Cao contributed equally to this work.

Concept and design: Wong, Cao, Lyons, Dutta, Ötleş, Singh.

Acquisition, analysis, or interpretation of data: Wong, Cao, Lyons, Dutta, Major, Singh.

Drafting of the manuscript: Wong, Cao, Major.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Wong, Cao, Lyons, Singh.

Administrative, technical, or material support: Wong, Ötleş.

Supervision: Dutta, Singh.

Conflict of Interest Disclosures: Dr Lyons reported receiving grants from the National Institutes of Health National Center for Advancing Translational Sciences and the Doris Duke Charitable Foundation and the Big Ideas Award from BJC HealthCare and Washington University. Mr Ötleş reported having a patent pending for the University of Michigan for an artificial intelligence–based approach for the dynamic prediction of the injured patient health state. Dr Singh reported receiving grants from Teva Pharmaceuticals and Blue Cross Blue Shield of Michigan. No other disclosures were reported.

Funding/Support: This study was supported by the University of Michigan Precision Health (Ms Cao), grant KL2TR002346 from the National Institutes of Health National Center for Advancing Translational Sciences (Dr Lyons), the Doris Duke Charitable Foundation Fund to Retain Clinical Scientists (Dr Lyons), and grant T32GM007863 from the National Institutes of Health National Institute of General Medical Sciences (to Mr Ötleş).

Role of the Funder/Sponsor: The University of Michigan Precision Health, the National Institutes of Health, and the Doris Duke Charitable Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
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Downing  NL , Rolnick  J , Poole  SF ,  et al.  Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation.   BMJ Qual Saf. 2019;28(9):762-768. doi:10.1136/bmjqs-2018-008765 PubMedGoogle ScholarCrossref
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Wong  A , Otles  E , Donnelly  JP ,  et al.  External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients.   JAMA Intern Med. 2021;181(8):1065-1070. doi:10.1001/jamainternmed.2021.2626 PubMedGoogle ScholarCrossref
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Ginestra  JC , Giannini  HM , Schweickert  WD ,  et al.  Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock.   Crit Care Med. 2019;47(11):1477-1484. doi:10.1097/CCM.0000000000003803 PubMedGoogle ScholarCrossref
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Eilish  MC , Watkins  EA . Repairing innovation: a study of integrating AI in clinical care. Data & Society Research Institute. Published September 2020. Accessed August 5, 2021. https://datasociety.net/wp-content/uploads/2020/09/Repairing-Innovation-DataSociety-20200930-1.pdf
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Finlayson  SG , Subbaswamy  A , Singh  K ,  et al.  The clinician and dataset shift in artificial intelligence.   N Engl J Med. 2021;385(3):283-286. doi:10.1056/NEJMc2104626 PubMedGoogle ScholarCrossref
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Singh  K , Valley  TS , Tang  S ,  et al  Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19.   Ann Am Thorac Soc. 2021;18(7):1129-1137. doi:10.1513/AnnalsATS.202006-698OCGoogle Scholar
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