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

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

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