Trial reportStudies in health technology and informatics2025
Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.
Trial report in Studies in health technology and informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.