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

Theoretical Frameworks and Concepts (355) Simulation (172) Clinical Information and Decision Making (2004) Decision Support for Health Professionals (1591) Case Study (35) Information Models (46) Clinical Informatics (1377)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43486, first published .
Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study

Optimizing the Implementation of Clinical Predictive Models to Minimize National Costs: Sepsis Case Study

Authors of this article:

Parker Rogers1 Author Orcid Image ;   Aaron E Boussina2 Author Orcid Image ;   Supreeth P Shashikumar2 Author Orcid Image ;   Gabriel Wardi3, 4 Author Orcid Image ;   Christopher A Longhurst2 Author Orcid Image ;   Shamim Nemati2 Author Orcid Image
Article Authors Cited by (10) Tweetations (13) Metrics

Journals

  1. Wardi G, Owens R, Josef C, Malhotra A, Longhurst C, Nemati S. Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us. Critical Care Medicine 2023;51(8):985 View
  2. Sasmito P, Pranata S, Pamungkas R, Emaliyawati E, Arifani N. Challenges of implementing the hour-1 sepsis bundle: a qualitative study from a secondary hospital in Indonesia. Acute and Critical Care 2024;39(4):545 View
  3. Lin T, Chung H, Jian M, Chang C, Lin H, Yen C, Tang S, Pan P, Perng C, Chang F, Chen C, Shang H. AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study. Journal of Medical Internet Research 2025;27:e56155 View
  4. Ramesh K, Boussina A, Shashikumar S, Malhotra A, Longhurst C, Josef C, Quintero K, Del Rosso J, Nemati S, Wardi G. Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey. Critical Care Explorations 2025;7(6):e1276 View
  5. Steel P, Wardi G, Harrington R, Longhurst C. Learning health system strategies in the AI era. npj Health Systems 2025;2(1) View
  6. Papareddy P, Lobo T, Holub M, Bouma H, Maca J, Strodthoff N, Herwald H. Transforming sepsis management: AI-driven innovations in early detection and tailored therapies. Critical Care 2025;29(1) View
  7. Álvarez-Vázquez J, Casal-Guisande M, Fernández-García A, Mosteiro-Añón M, Torres-Durán M, Fernández-Villar A. Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review. Healthcare 2025;13(22):2933 View
  8. Dorosan M, Chen Y, He Y, Zhuang Q, Lam S. In silico evaluation of algorithm-based clinical decision support systems based on care pathway simulation models: A scoping review (Preprint). JMIR AI 2025 View
  9. Raszke P, Giebel G, Wasem J, Adamzik M, Nowak H, Palmowski L, Heinz P, Timmesfeld N, Tokic M, Brunkhorst F, Blase N. Patient Benefits in the Context of Sepsis-Related AI-Based Clinical Decision Support Systems: Scoping Review. Journal of Medical Internet Research 2026;28:e76772 View

Conference Proceedings

  1. Givanoudi E, Vrochidou E, Papakostas G. 2024 International Conference on Circuit, Systems and Communication (ICCSC). Evaluating the Reliability of Artificial Intelligence in Healthcare: The Doctors’ Perspective in Northern Greece View