This paper is in the following e-collection/theme issue:

Cognition and Information Processing in Human Factors Research (11) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Clinical Informatics (1022) User-centred Design Case Studies (596) Design and Usability of Clinical Software and EHRs (268)

Published on in Vol 9, No 4 (2022): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36976, first published .
Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians

Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians

Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians

Authors of this article:

Jasmine A Silvestri1 Author Orcid Image ;   Tyler E Kmiec1 Author Orcid Image ;   Nicholas S Bishop1 Author Orcid Image ;   Susan H Regli2 Author Orcid Image ;   Gary E Weissman1, 3, 4, 5 Author Orcid Image
Article Authors Cited by (5) Tweetations (14) Metrics

Journals

  1. Lazzarino R, Borek A, Honeyford K, Welch J, Brent A, Kinderlerer A, Cooke G, Patil S, Gordon A, Glampson B, Goodman P, Ghazal P, Daniels R, Costelloe C, Tonkin-Crine S. Views and Uses of Sepsis Digital Alerts in National Health Service Trusts in England: Qualitative Study With Health Care Professionals. JMIR Human Factors 2024;11:e56949 View
  2. Esposito P, Cappadona F, Marengo M, Fiorentino M, Fabbrini P, Quercia A, Garzotto F, Castellano G, Cantaluppi V, Viazzi F. Recognition patterns of acute kidney injury in hospitalized patients. Clinical Kidney Journal 2024;17(8) View
  3. Owoyemi A, Okpara E, Salwei M, Boyd A. End user experience of a widely used artificial intelligence based sepsis system. JAMIA Open 2024;7(4) View