The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Rule-Based Artificial Intelligence and Workflow to Prompt Early Sepsis Management: A Quality Improvement Project
Abstract
Introduction Early identification and management of sepsis improves patient outcomes, yet hospitals struggle to consistently screen patients on arrival and during hospitalization. The Centers for Disease Control published Hospital Sepsis Program Core Elements to guide hospital sepsis management and outcomes improvement efforts and will measure the sepsis core elements with the National Healthcare Safety Network Annual Hospital Survey. Methods To further sepsis care management, our community-owned, nonprofit hospital implemented an emergency department quality improvement project, introducing rule-based artificial intelligence (AI) for sepsis identification with a workflow. Objectives were to measure rule-based AI sensitivity, sepsis management compliance, length of stay (LOS), and mortality rate. Results A total of 895 cases were included in the final dataset, 370 preimplementation and 525 postimplementation. Postimplementation rule-based AI alerts identified 93.9% (493 of 525) cases for sepsis management interventions. After rule and workflow implementation, combined 3-hour compliance for antibiotic given, blood culture drawn, and lactate measured was 89.5%. Average LOS decreased by 2.3 days ( p Conclusions Implementing rule-based AI software to identify severe sepsis in conjunction with a sepsis workflow decreased LOS for patients diagnosed with either severe sepsis or septic shock.
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“A few studies have shown that AI sepsis prediction models were associated with decreased patient mortality; however, they were observational cohorts or before-and-after designs [55,56,57].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [57]
Citations
Cites
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Evans, L., Rhodes, A., Alhazzani, W., Antonelli, M., Coopersmith, C. M., French, C., Machado, F. R., Mcintyre, L., Ostermann, M., Prescott, H. C., et al. (2021) · Critical Care Medicine · doi:10.1097/ccm.0000000000005337 · Cited by 2,935
Kulikowski, C. A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677895 · Cited by 144
Sepsis prediction model for determining sepsis vs SIRS, qSOFA, and SOFA
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The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective
Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R., & Elkin, P. (2024) · Life · doi:10.3390/life14060652 · Cited by 76
Flagged first, then by citations
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Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis
Nguyen, H. B., Krishtopaytis, E., Lopez, E., Farnoudi, N., Van, T., Kharalampova, V., & Coz Yataco, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15093477
“A few studies have shown that AI sepsis prediction models were associated with decreased patient mortality; however, they were observational cohorts or before-and-after designs [55–57].”
Metadata
- Authors
- Grooms, E., Biesack, K., Abban, B., & Kramer, J.
- ORCID
- Kramer, Joan
- Venue
- Journal for Healthcare Quality
- Published
- 2025-09-15
- Volume
- 47
- Issue
- 4
- ISSN
- 1062-2551 (ISSN-L)
- 1945-1474
- OpenAlex
- W4414200225
- Cited by
- 1
- References
- 4
- Citations by year
- 2026: 1
- FWCI
- 0.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal for Healthcare Quality (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- sepsis, rule-based artificial intelligence, sepsis recognition, emergency department, sepsis mortality, length of hospital stay, antibiotic administration, blood culture, lactate measurement, septic shock, quality improvement
- MeSH terms
- Artificial Intelligence, Emergency Service, Hospital, Female, Humans, Length of Stay, Male, Middle Aged, Sepsis, Workflow, Quality Improvement
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