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A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
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Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a
Mugdha Joshi1, Keizra Mecklai2, Ronen Rozenblum2,3
1Department of Medicine, Stanford University, Stanford, California, USA.
JAMIA Open
|April 27, 2022
Summary
Implementing sepsis clinical decision support (CDS) tools, including rule-based (RB) and machine learning (ML) models, presents challenges. Clinician acceptance and understanding, especially for ML models, are key to successful sepsis surveillance.
Area of Science:
- Healthcare Informatics
- Clinical Decision Support Systems
- Sepsis Management
Background:
- Numerous sepsis surveillance clinical decision support (CDS) tools exist, including vendor-provided, third-party, and in-house solutions utilizing rule-based (RB) and machine learning (ML) algorithms.
- Implementation of these tools is complex, involving interdisciplinary teams and diverse workflow integrations.
Purpose of the Study:
- To explore the motivations, tool selection criteria, and implementation experiences of leaders overseeing sepsis CDS implementation.
- To identify challenges and successful strategies for implementing sepsis CDS, particularly distinguishing between RB and ML models.
Main Methods:
- Semi-structured interviews and questionnaires administered to 21 hospital leaders at 15 US medical centers.
- Inductive thematic analysis of coded responses from participants.
Main Results:
- Motivations for sepsis CDS include quality metrics; tool choice is influenced by integration ease, customization, and perceived predictive power.
- Implementation is complex and time-consuming, with varied tool choices and workflow integration. Clinician buy-in and alert optimization are crucial.
- Machine learning (ML) models faced more distrust and confusion compared to rule-based (RB) models, necessitating strategies to address user understanding and acceptance.
Conclusions:
- Shared socio-technical challenges exist for both RB and ML sepsis CDS implementation.
- Improving ML model feasibility and effectiveness in quality improvement requires focused user education, support, expectation management, and dissemination of best practices.
- Clinician acceptance is a significant barrier; successful implementation of less intuitive ML models demands attention to user confusion and distrust.
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