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Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic
Rachel Y Lee1, Kenrick D Cato2,3, Patricia C Dykes4,5
1Columbia University, Department of Biomedical Informatics, New York, NY.
Studies in Health Technology and Informatics
|August 8, 2025
Summary
The CONCERN Early Warning System (EWS), an AI tool, significantly reduced inpatient mortality and length of stay. Clinician engagement with the EWS detailed display varied by role, shift, and unit type, offering insights for optimizing AI implementation in healthcare.
Area of Science:
- Healthcare Informatics
- Clinical Decision Support Systems
- Artificial Intelligence in Medicine
Background:
- Hospitalized patients face risks of clinical deterioration.
- Early detection and intervention are crucial for improving patient outcomes.
- Existing early warning systems (EWS) can be enhanced with AI for better predictive accuracy.
Purpose of the Study:
- To analyze clinician engagement patterns with the CONCERN Early Warning System (EWS) detailed display.
- To understand how user interactions with AI-driven clinical decision support vary.
- To identify factors influencing the use of the CONCERN EWS to optimize its implementation and impact.
Main Methods:
- Retrospective analysis of electronic health record (EHR) log-file data.
- Examined 2,572 instances of CONCERN detailed display launches by 393 clinicians.
- Data stratified by clinician role, patient comorbidity, site, shift, and unit type.
Main Results:
- Registered nurses and ordering providers showed balanced engagement with the CONCERN detailed display.
- Usage was predominantly during day shifts (89.9%) and in acute care units (83.5%).
- Distinct usage patterns were observed based on clinician role, patient, and care setting characteristics.
Conclusions:
- Clinician engagement with the CONCERN EWS is influenced by various factors, including user role and care environment.
- Understanding these usage patterns is key to refining implementation strategies for AI-driven tools.
- Optimizing clinician interaction with AI decision support can enhance its effectiveness in improving patient outcomes.
Keywords:
Clinical decision support systemEarly warning systemElectronic health recordsExplainable AINursing informaticsUser engagementMore Related Videos
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