Trial reportJournal of hospital medicine2014
A randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team.
Trial report in Journal of hospital medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 6 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
- Effects of Real-Time Automated Clinical Deterioration Alert and Trigger Systems on Clinical Outcomes in Adult General Ward Patients: A Systematic Review and Meta-Analysis.Critical care medicine · 2026Pooled it
- Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Early warning systems and rapid response systems for the prevention of patient deterioration on acute adult hospital wards.The Cochrane database of systematic reviews · 2021Pooled it
- Statistical Modeling and Aggregate-Weighted Scoring Systems in Prediction of Mortality and ICU Transfer: A Systematic Review.Journal of hospital medicine · 2019Pooled it
- The effects of on-screen, point of care computer reminders on processes and outcomes of care.The Cochrane database of systematic reviews · 2009Pooled it
- AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.Nature medicine · 2024Trial
- Clinical Effectiveness of AI-Based Cardiac Arrest Prediction System for General Ward Patients (VITAL-MD): Protocol for a Multicenter Pragmatic Stepped Wedge Cluster Randomized Controlled Trial.JMIR research protocols · 2026Article
- Determining a Clinically Applicable Cutoff in AI Algorithms for Predicting Clinical Deterioration: A Workload-Constrained, Alarm-Based Approach.Journal of clinical medicine · 2026Article
- Association of Vital Sign and Laboratory Abnormalities Detected via Remote Monitoring with ICU Readmission and Mortality: An Observational Cohort Study.Joint Commission journal on quality and patient safety · 2026Observational
- Clinical Effectiveness of an Artificial Intelligence-Based Prediction Model for Cardiac Arrest in General Ward-Admitted Patients: A Non-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2026Article
- Treatment Recommendations for Clinical Deterioration on the Wards: Development and Validation of Machine Learning Models.JMIR AI · 2026Article
- Artificial intelligence in clinical decision support and the prediction of adverse events.Frontiers in digital health · 2025Review
- Clinical evaluation of a machine learning-based early warning system for patient deterioration.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2024Article
- Expanding Critical Care Delivery beyond the Intensive Care Unit: Determining the Design and Implementation Needs for a Tele-Critical Care Consultation Service.Applied clinical informatics · 2024Article
- Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study.JAMIA open · 2022Article
- Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis.Frontiers in medicine · 2022Article
- Incorporating a real-time automatic alerting system based on electronic medical records could improve rapid response systems: a retrospective cohort study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundEpisodes of patient deterioration on hospital units are expected to increasingly contribute to morbidity and healthcare costs.
objectiveTo determine if real-time alerts sent to the rapid response team (RRT) improved patient care.
designRandomized, controlled trial.
settingEight medicine units (Barnes-Jewish Hospital). PATIENTS: Five hundred seventy-one patients.
interventionReal-time alerts generated by a validated deterioration algorithm were sent real-time to the RRT (intervention) or hidden (control). MEASUREMENTS: Intensive care unit (ICU) transfer, hospital mortality, hospital duration.
resultsICU transfer (17.8% vs 18.2%; odds ratio: 0.972; 95% confidence interval [CI]: 0.635-1.490) and hospital mortality (7.3% vs 7.7%; odds ratio: 0.947; 95% CI: 0.509-1.764) were similar for the intervention and control groups. The number of patients requiring transfer to a nursing home or long-term acute care hospital was similar for patients in the intervention and control groups (26.9% vs 26.3%; odds ratio: 1.032; 95% CI: 0.712-1.495). Hospital duration (8.4 ± 9.5 days vs 9.4 ± 11.1 days; P = 0.038) was statistically shorter for the intervention group. The number of RRT calls initiated by the primary care team was similar for the intervention and control groups (19.9% vs 16.5%; odds ratio: 1.260; 95% CI: 0.823-1.931).
conclusionsReal-time alerts sent to the RRT did not reduce ICU transfers, hospital mortality, or the need for subsequent long term care. However, hospital length of stay was modestly reduced.
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.