Trial reportScientific reports2026
A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.
Trial report in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04359641 (Predictive Monitoring - IMPact in Acute Care Cardiology Trial), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Predictive Monitoring - IMPact in Acute Care Cardiology Trial
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline 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
- Artificial intelligence in cardiology: an evidence map across subspecialties, 2014-26.European heart journal. Digital health · 2026Article
- 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
- Implementation of an AI-Based Clinical Decision Support System Predicting In-Hospital Cardiac Arrest in General Wards: A Multicenter Staggered-Implementation Study in Secondary Hospitals in Korea.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics on hours free of clinical deterioration events among medical and surgical patients in an acute care cardiology medical-surgical ward. 10,422 inpatient visits were randomly assigned by cluster to the intervention group of a display of risk trajectories or to a control group of usual medical care. The trial was undertaken on an 85-bed inpatient cardiology and cardiac surgery ward of an academic hospital with a substantial implementation and education plan. This was a passive display with no specific response mandated. The primary analysis compared events of clinical deterioration (death, emergent ICU transfer, emergent endotracheal intubation, cardiac arrest, or emergent surgery) and compared mortality 21 days after admission. Patients with a large spike in risk score had, on average, twice the length of hospital stay (6.8 compared to 3.4 days). There was no change in the primary outcome between groups. Among those who had a clinical event, there were more event-free hours in the intervention/display-on group compared to the standard-of-care/display-off, but this did not reach statistical significance. Clinicians chose to transfer 11% of patients into or out of display beds, a censoring event removing them from the analysis, thereby undermining aspects of the randomized nature of the study. Predictive analytics monitoring incorporating continuous cardiorespiratory monitoring and displays of risk trajectories coupled with an education plan did not improve patient outcomes. While necessary to conduct the study, the pragmatic design allowed for significant movement towards intervention/displayed beds for sicker patients. Design considerations in the future must focus on understanding clinicians' interpretation, care processes, and communication practices.Clinical trial registration number: NCT04359641 Registered 4/24/20.
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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.