Rule-Based Artificial Intelligence and Workflow to Prompt Early Sepsis Management: A Quality Improvement Project

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.

Authors

Joan Kramer (ORCID: https://orcid.org/0000-0003-4644-5681)
Emily Grooms
Karen Biesack
Bart Abban

Institutions

Emerson (Sweden) (SE)

Publication Details

Journal: Journal for Healthcare Quality
Published: 2025-09-15
DOI: https://doi.org/10.1097/jhq.0000000000000493
Primary Topic: Sepsis Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 0.00

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article

Rule-Based Artificial Intelligence and Workflow to Prompt Early Sepsis Management: A Quality Improvement Project

Joan Kramer, Emily Grooms, Karen Biesack, Bart Abban

Sep 15, 2025

Journal for Healthcare Quality

Sepsis Diagnosis and Treatment

article

Rule-Based Artificial Intelligence and Workflow to Prompt Early Sepsis Management: A Quality Improvement Project

Joan Kramer, Emily Grooms, Karen Biesack, Bart Abban

September 15, 2025article en

Abstract

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.

Journal for Healthcare QualityVol. 47(4)

Emerson (Sweden) (SE)

Openalex Percentile: Top 66%

Sepsis Diagnosis and Treatment

DOI Source OpenAlex

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Rule-Based Artificial Intelligence and Workflow to Prompt Early Sepsis Management: A Quality Improvement Project — Joan Kramer, Emily Grooms, et al. · Journal for Healthcare Quality (2025) | TGRS Research Map | TGRS