Trial reportNature medicine2025
Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.
Trial report in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT03911687 (Communicating Narrative Concerns Entered by RNs), which is not on this map. Cited by 31 papers, 2 of them syntheses that pooled it.
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.
Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Clinical Surveillance Technologies in Nonintensive Care Unit Hospital Settings: Systematic Review and Bayesian Network Meta-Analysis of Randomized Trials.Journal of medical Internet research · 2026Pooled it
- Operationalization of Artificial Intelligence Applications in the Intensive Care Unit: A Systematic Review.JAMA network open · 2025Pooled it
- Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.Studies in health technology and informatics · 2025Trial
- Earlier ICU Transfer after CONCERN Early Warning System Score Escalation Reduced Sepsis-related Mortality: Results from a Multi-site Pragmatic Cluster Randomized Controlled Trial.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Trial
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- Determining a Clinically Applicable Cutoff in AI Algorithms for Predicting Clinical Deterioration: A Workload-Constrained, Alarm-Based Approach.Journal of clinical medicine · 2026Article
- Mapping ICU nursing intervention notes to clinical care classification using a large language model: an evaluation study.BMC nursing · 2026Article
- STDP-inspired temporal transition modeling for adaptive clinical risk prediction from electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Enhancing prediction of inpatient deterioration by combining clinical and nurse concern features, with or without temporal clustering.JAMIA open · 2026Article
- Bridging the governance gap in health artificial intelligence: integrating nursing perspectives into the National Academy of Medicine Artificial Intelligence Code of Conduct.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- Is AI actually improving healthcare?Nature medicine · 2026Article
- Turkish version of the Nurse Intuition Patient Deterioration Scale: cross-cultural adaptation and validation study.BMC nursing · 2026Article
- Development and preliminary evaluation of an AI-enhanced three-dimensional integrated quality model for quality-sensitive indicators in operating room management: a prospective single-center study.Frontiers in medicine · 2026Article
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
- The impact of leadership on AI deployment study outcomes in healthcare: an integrative analysis.NPJ digital medicine · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Goal-directed perioperative haemodynamic therapy: need for a modern perioperative care loop.British journal of anaesthesia · 2025Article
- Reliability and validity testing the Chinese version of the nursing surveillance scale: a cross-sectional study.BMC nursing · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
21 authors.
Funding
Abstract
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pragmatic trial with cluster-randomization of 74 clinical units (37 intervention; 37 usual care) across 2 health systems. Eligible adult hospital encounters were included. We tested if outcomes differed between patients whose care teams were and patients whose care teams were not informed by the CONCERN EWS. Coprimary outcomes were in-hospital mortality (examined as instantaneous risk) and length of stay. Secondary outcomes were cardiopulmonary arrest, sepsis, unanticipated intensive care unit transfers and 30-day hospital readmission. Among 60,893 hospital encounters (33,024 intervention; 27,869 usual care), intervention group encounters had 35.6% decreased instantaneous risk of death (adjusted hazard ratio (HR), 0.64; 95% confidence interval (CI), 0.53-0.78; P < 0.0001), 11.2% decreased length of stay (adjusted incidence rate ratio, 0.91; 95% CI, 0.90-0.93; P < 0.0001), 7.5% decreased instantaneous risk of sepsis (adjusted HR, 0.93; 95% CI, 0.86-0.99; P = 0.0317) and 24.9% increased instantaneous risk of unanticipated intensive care unit transfer (adjusted HR, 1.25; 95% CI, 1.09-1.43; P = 0.0011) compared with usual-care group encounters. No adverse events were reported. A machine learning-based EWS, modeled on nursing surveillance patterns, decreased inpatient deterioration risk with statistical significance. ClinicalTrials.gov registration: NCT03911687 .
Indexed as
Identifiers
What OpenQuestion holds
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.