Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.
Journal:
Studies in health technology and informatics
Published Date:
Aug 7, 2025
Abstract
The CONCERN Early Warning System (EWS) uses artificial intelligence (AI) to analyze nursing documentation patterns, predicting hospitalized patients' risk of clinical deterioration. It generates real-time risk scores displayed on the electronic health record (EHR) interface for the inpatient care team, enhancing situational awareness and supporting timely interventions. A recent multi-site pragmatic cluster randomized controlled trial demonstrated a 35.6% reduction in inpatient mortality, an 11.2% decrease in length of stay, and improved outcomes for patients requiring unplanned ICU transfers. To gain better insights on the mechanisms driving these positive outcomes, we examined clinicians' engagement with the CONCERN detailed display by clinical role, patient comorbidity level, site, shift, and unit type. This retrospective analysis of EHR log-file data examined 2,572 instances of CONCERN detailed display launches by 393 unique clinician users. Our findings showed distinct usage patterns influenced by clinician role, site, shift, unit type, and patient characteristics. Notably, registered nurses (59%) and ordering providers (37.7%) demonstrated balanced engagement. CONCERN detailed display launches were predominantly observed during day shifts (89.9%) and in acute care units (83.5%), aligning with workflows that prioritize interprofessional decision-making and timely care escalations. These findings offer valuable insights into how user interactions with this AI-driven clinical decision support tool may vary based on clinician, patient, and care setting characteristics, highlighting opportunities to refine implementation strategies and optimize its impact on clinical outcomes.
