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

Digital Health Reviews (1724) Decision Support for Health Professionals (1539) Clinical Informatics (1325) Electronic Health Records (1319) Clinical Information and Decision Making (1915)

Published on in Vol 24, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31083, first published .
Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review

Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review

Authors of this article:

Khalia Ackermann1 Author Orcid Image ;   Jannah Baker1 Author Orcid Image ;   Malcolm Green2 Author Orcid Image ;   Mary Fullick2 Author Orcid Image ;   Hilal Varinli3 Author Orcid Image ;   Johanna Westbrook1 Author Orcid Image ;   Ling Li1 Author Orcid Image
Article Authors Cited by (22) Tweetations (18) Metrics

Journals

  1. Srimedha B, Naveen Raj R, Mayya V. A Comprehensive Machine Learning Based Pipeline for an Accurate Early Prediction of Sepsis in ICU. IEEE Access 2022;10:105120 View
  2. Ackermann K, Baker J, Festa M, McMullan B, Westbrook J, Li L. Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Pediatric, Neonatal, and Maternal Inpatients: Scoping Review. JMIR Medical Informatics 2022;10(5):e35061 View
  3. Dewitte K, Scheurwegs E, Van Ierssel S, Jansens H, Dams K, Roelant E. Audit of a computerized version of the Manchester triage system and a SIRS-based system for the detection of sepsis at triage in the emergency department. International Journal of Emergency Medicine 2022;15(1) View
  4. van der Vegt A, Scott I, Dermawan K, Schnetler R, Kalke V, Lane P. Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. Journal of the American Medical Informatics Association 2023;30(7):1349 View
  5. Huang S, Liang Y, Li J, Li X. Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review. Journal of Medical Internet Research 2023;25:e51024 View
  6. Shakibaei Bonakdeh E, Sohal A, Rajabkhah K, Prajogo D, Melder A, Nguyen D, Bingham G, Tong E. Influential factors in the adoption of clinical decision support systems in hospital settings: a systematic review and meta-synthesis of qualitative studies. Industrial Management & Data Systems 2024;124(4):1463 View
  7. Nates J, Pène F, Darmon M, Mokart D, Castro P, David S, Povoa P, Russell L, Nielsen N, Gorecki G, Gradel K, Azoulay E, Bauer P. Septic shock in the immunocompromised cancer patient: a narrative review. Critical Care 2024;28(1) View
  8. 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
  9. Giger O, Pfitzer E, Mekniran W, Gebhardt H, Fleisch E, Jovanova M, Kowatsch T. Digital health technologies and innovation patterns in diabetes ecosystems. DIGITAL HEALTH 2025;11 View
  10. Barbieri D, Srinivasan D, Ulrich J, Ranganathan S, Chang C, Gerac J, Cha J. Systems-based framework for clinical decision-support system integration for patient sepsis management: A theoretical application of the SEIPS model. Human Factors in Healthcare 2025;7:100098 View
  11. Gao Y, Chen H, Wu R, Zhou Z. AI-driven multi-omics profiling of sepsis immunity in the digestive system. Frontiers in Immunology 2025;16 View
  12. Chaganti S, Singh V, Gent A, Kamaleswaran R, Kamen A. Evaluating the impact of common clinical confounders on performance of deep-learning-based sepsis risk assessment. Frontiers in Artificial Intelligence 2025;8 View
  13. Helleberg J, Sundelin A, Mårtensson J, Rooyackers O, Thobaben R. Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning. BMC Medical Informatics and Decision Making 2025;25(1) View
  14. Nimri R, Phillip M. Enhancing Care in Type 1 Diabetes with Artificial Intelligence Driven Clinical Decision Support Systems. Hormone Research in Paediatrics 2025;98(4):384 View
  15. Kanter E, Güler E, Kırık S, Şahan T, Baygın M, Altınöz E, Bora E, Karakaya Z. Improving Prognostic Accuracy of MASCC Score with Lactate and CRP Measurements in Febrile Neutropenic Patients. Diagnostics 2025;15(15):1922 View
  16. Brunkhorst F, Adamzik M, Axer H, Bauer M, Bode C, Bone H, Brenner T, Bucher M, David S, Dietrich M, Eckmann C, Elke G, Esser T, Felbinger T, Geffers C, Gerlach H, Grabein B, Gründling M, Günther U, Hagel S, Hecker A, Henkel S, Janusan B, John S, Jörres A, Kaasch A, Kluge S, Kochanek M, Lajca A, Marx G, Mayer K, Meybohm P, Mörer O, Oppert M, Patchev V, Pletz M, Putensen C, Rahmel T, Rosendahl J, Rossaint R, Salzberger B, Sander M, Schaller S, Scharf-Janssen C, Schmitt F, Unterberg M, Weigand M, Weimann A, Weis S, Weiß B, Wolf A, Zarbock A. S3-Leitlinie Sepsis – Prävention, Diagnose, Therapie und Nachsorge – Update 2025. Medizinische Klinik - Intensivmedizin und Notfallmedizin 2025 View
  17. Seckel M, Lejnieks J. Sepsis Identification Tools: A Narrative Review. Critical Care Nurse 2025;45(5):63 View
  18. Azarmina H, Keikhaei N, Kharazmi K. From empirical to precision therapy in ICUs: Rethinking antibiotic use after COVID-19. Journal of Current Biomedical Reports 2025:88 View
  19. Mirmotahari S, maghsoudi A, Amini M, Safari M, Akrami M, Mirnezami S, Najafi A, Kianpour P, Mojtahedzadeh M, Hassani S. Sepsis diagnosis and monitoring: Frontiers in innovative technology. Clinica Chimica Acta 2026;579:120640 View
  20. Kim J, Lui B, Goldstein P, Rubin J, White R, Jotwani R. From Data to Decisions: Harnessing Multi-Agent Systems for Safer, Smarter, and More Personalized Perioperative Care. Journal of Personalized Medicine 2025;15(11):540 View

Books/Policy Documents

  1. Warner E, Al-Turkestani N, Bianchi J, Gurgel M, Cevidanes L, Rao A. Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging. View
  2. Sa M, Crespo R. The Sepsis Codex. View