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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021.
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
Schertz, A. R., Lenoir, K. M., Bertoni, A. G., Levine, B. J., Mongraw-Chaffin, M., & Thomas, K. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.29729 · Cited by 78
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
Research works printed but not linked: 11
- Rhee, 2017, Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014, JAMA, 318, 1241, 10.1001/jama.2017.13836 · DOI · Paper page
- Dantes, 2023, Sepsis program activities in acute care hospitals—National Healthcare Safety Network, United States, 2022, MMWR Morb Mortal Wkly Rep, 72, 907, 10.15585/mmwr.mm7234a2 · DOI
- Rhodes, 2015, The Surviving Sepsis Campaign bundles and outcome: Results from the International Multicentre Prevalence Study on Sepsis (the IMPreSS study), Intensive Care Med, 41, 1620, 10.1007/s00134-015-3906-y · DOI
- Barbash, 2019, National performance on the Medicare SEP-1 sepsis quality measure, Crit Care Med, 47, 1026, 10.1097/CCM.0000000000003613 · DOI · Paper page
- Jorgensen, 2019, Nurse influence in meeting compliance with the Centers for Medicare and Medicaid Services quality measure: Early management bundle, severe sepsis/septic shock (SEP1), Dimens Crit Care Nurs, 38, 70, 10.1097/DCC.0000000000000340 · DOI
- Kim, 2024, Sepsis alert systems, mortality, and adherence in emergency departments: A systematic review and meta-analysis, JAMA Netw Open, 7, e2422823, 10.1001/jamanetworkopen.2024.22823 · DOI
- Kang, 2021, The impact of an inpatient nurse-triggered sepsis alert on antimicrobial utilization, Jt Comm J Qual Patient Saf, 47, 157
- Gonzalez-Smith, 2022, How health systems decide to use artificial intelligence for clinical decision support, NEJM Catalyst, 3, 10.1056/CAT.21.0416 · DOI
- Vincent, 2014, Early management of sepsis, Clin Exp Emerg Med, 1, 3, 10.15441/ceem.14.005 · DOI
- Shankar-Hari, 2016, Developing a new definition and assessing new clinical criteria for septic shock: For the third international consensus definitions for sepsis and septic shock (sepsis-3), JAMA, 315, 775, 10.1001/jama.2016.0289 · DOI
- Bauer, 2019, Inter-rater agreement for abstraction of the early management bundle, severe sepsis/septic shock (SEP-1) quality measure in a multi-hospital health system, Jt Comm J Qual Patient Saf, 45, 108
Cited by
Sorted by citations
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
