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Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Sarah C Rossetti^1,2, Patricia C Dykes^3,4, Chris Knaplund¹

¹Columbia University Irving Medical Center, Department of Biomedical Informatics, New York, NY.

Medrxiv : the Preprint Server for Health Sciences

|June 17, 2024

Summary

The CONCERN Early Warning System (EWS), using nursing documentation, significantly reduced patient mortality and length of stay. This system improves early detection of patient deterioration, enhancing clinical outcomes.

Keywords:

Early warning system artificial intelligence clinical decision support electronic health record inpatient machine learning nurses nursing informatics randomized controlled trial

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Area of Science:

Clinical Informatics
Patient Safety
Healthcare Quality Improvement

Background:

Delayed detection of patient deterioration often stems from limited data in early warning systems (EWS) and poor team situational awareness.
This can lead to delayed clinical interventions and poorer patient outcomes.

Purpose of the Study:

To evaluate the impact of the COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) on patient mortality and length of stay.
To test the hypothesis that informing care teams with CONCERN EWS data reduces mortality and length of stay.

Main Methods:

A one-year, multisite pragmatic clinical trial involving cluster-randomization of acute and intensive care units.
The CONCERN EWS intervention utilized machine learning on real-time nursing surveillance documentation patterns to predict patient deterioration risk.
Adult patients in acute and intensive care units were included, excluding those on comfort care or with specific advance directives.

Main Results:

The intervention group (CONCERN EWS) showed a 35.6% decreased risk of in-hospital death and an 11.2% decreased length of stay compared to the usual-care group.
A 7.5% decrease in sepsis risk was observed in the intervention group.
An increased risk of unanticipated ICU transfer (24.9%) was noted in the intervention group.

Conclusions:

Integrating a hospital-wide EWS based on nursing surveillance patterns into the EHR workflow significantly reduces in-hospital mortality, sepsis incidence, and length of stay.
The CONCERN EWS demonstrates the potential to improve patient safety and healthcare efficiency through enhanced early detection of deterioration.