SynthesisBMC medical informatics and decision making2025
AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Pathophysiology-guided biomarkers and therapeutics for precision trauma medicine in polytrauma with musculoskeletal injuries.Military Medical Research · 2026Review
- Shock index and modified shock index at discharge as predictors of long-term mortality after myocardial infarction: results from the Augsburg Myocardial Infarction Registry.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Current status and future outlook of artificial intelligence and automation in intravenous infusion systems.NPJ digital medicine · 2026Review
- Decision-centered artificial intelligence for perioperative care outside the operating room: a practical review for surgeons.Journal of minimally invasive surgery · 2026Review
- Review
- Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis.Journal of medical Internet research · 2026Article
- Evaluation and Comparison of Latent Health Risk Prediction Models for Clinical Triage: Protocol for a Mixed Methods Study.JMIR research protocols · 2026Article
- An Agentic LLM Framework for Autonomous Surgical Continuum Monitoring: ReAct-Driven Tool-Use Agents for Presurgical, Intraoperative, and Postsurgical Cardiopulmonary Care.Bioengineering (Basel, Switzerland) · 2026Article
- Can health technologies be "care optimizers"? A normative evaluation of digital health technologies in light of postphenomenological reflections.Medicine, health care, and philosophy · 2026Article
- The Mediation of AI Trust on AI Uncertainties and AI Competence Among Nurses: A Cross-Sectional Study.Journal of advanced nursing · 2026Article
- Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units.NPJ digital medicine · 2026Article
- Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.NPJ digital medicine · 2026Article
- A High-Precision Time-Varying Survival Model for Early Prediction of Patient Deterioration: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- From behavioral signals to therapy management: a real-world pilot implementation of human-in-the-loop AI workflow in proactive patient care.Frontiers in digital health · 2026Article
- Bridging the last mile in AI-driven patient safety: the missing link of trainee readiness.Annals of medicine and surgery (2012) · 2026Article
- Strengthening mental health care in Somalia through artificial intelligence: opportunities, challenges, and a roadmap for implementation.Frontiers in digital health · 2026Article
- Healthcare Systems at the Intersection of Just Culture and Artificial Intelligence: Emerging Challenges for Nursing Management.Risk management and healthcare policy · 2026Article
- Data-driven closed-loop health education: constructing an integrated assessment-intervention-feedback pathway for digital COPD management-a perspective.Frontiers in public health · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has limitations, prompting the exploration of AI-based predictive models as a means to enhance predictive accuracy and, consequently, patient outcomes.
methodsA systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. Databases including PubMed, and Web of Science were searched for relevant studies as of April 8, 2024. Studies were selected based on predefined criteria, specifically targeting AI-based models designed to predict in-hospital clinical deterioration.
resultsA total of five studies met the inclusion criteria, all of which underwent prospective clinical validation. These studies demonstrated that AI-based models significantly reduced in-hospital and 30-day mortality rates. Although a downward trend in ICU transfers was observed, the results were not statistically significant. Additionally, the use of AI models shortened overall hospital stays but resulted in a significant increase in ICU length of stay.
conclusionThe findings suggest that AI-based early warning models positively impact patient outcomes in real-world clinical settings. Despite the potential benefits, the effectiveness and real-world applicability of these models require further research. Challenges such as clinician adherence to AI warnings remain to be addressed.
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.