ArticleCMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne2024
Clinical evaluation of a machine learning-based early warning system for patient deterioration.
Article in CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated Features, Algorithms, and Technologies of Electronic Early Warning/Track-and-Trigger Systems: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Machine Learning in Causal Effect Estimation and Causal Evaluation of ML-Enabled Interventions in Nursing-Related Research: A Scoping Review.Nursing & health sciences · 2026Article
- A Realist Evaluation of a Rapid Response System for Mental State Deterioration in Acute Hospital Settings.Journal of advanced nursing · 2026Article
- MultiCardioNet: A Multimodal Deep Learning Model for Early Cardiovascular Deterioration Prediction in ICU Patients.Bioengineering (Basel, Switzerland) · 2026Article
- The Risks to Patients Associated with Using Artificial Intelligence Tools in Pharmacy Practice: A Scoping Review.Pharmacy (Basel, Switzerland) · 2026Review
- Clinical predictive artificial intelligence evaluation: A narrative review of trial designs and practical considerations.PLOS digital health · 2026Review
- Fixing Work, Not Workers: Leveraging Artificial Intelligence to Combat Burnout in Canadian Healthcare.Healthcare management forum · 2026Article
- Letter to the Editor: 'Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians'.Clinical medicine (London, England) · 2026Article
- Theoretical applications of artificial intelligence in smart infusion pump technology: Expert panel insights.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists · 2026Article
- Using data and artificial intelligence to improve care pathways of older people experiencing falls and frailty: Opportunities, challenges and practical considerations for clinicians.Clinical medicine (London, England) · 2026Review
- Advancing Psychiatric Safety With the Predictive Risk Identification for Mental Health Events Tool: Retrospective Cohort Study.JMIR mental health · 2026Article
- From patient voices to policy: Data analytics reveals patterns in Ontario's hospital feedback.PLOS digital health · 2026Article
- Climate resilience as a strategic lever for long-term healthcare sustainability: a narrative review.British journal of anaesthesia · 2026Review
- Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care.Frontiers in medicine · 2026Article
- Evaluating sociodemographic bias in a deployed machine-learned patient deterioration model.JAMIA open · 2025Article
- Deep Learning-Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study.JMIR medical informatics · 2025Article
- Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques.Scientific reports · 2025Article
- Machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients: a bidirectional cross-validation study.Frontiers in cardiovascular medicine · 2025Article
- Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions.Frontiers in digital health · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-time, machine learning-based early warning system for patient deterioration used in the general internal medicine (GIM) unit of an academic medical centre.
methodsIn this nonrandomized, controlled study, we evaluated the association between the implementation of a machine learning-based early warning system and clinical outcomes. We used propensity score-based overlap weighting to compare patients in the GIM unit during the intervention period (Nov. 1, 2020, to June 1, 2022) to those admitted during the pre-intervention period (Nov. 1, 2016, to June 1, 2020). In a difference-indifferences analysis, we compared patients in the GIM unit with those in the cardiology, respirology, and nephrology units who did not receive the intervention. We retrospectively calculated system predictions for each patient in the control cohorts, although alerts were sent to clinicians only during the intervention period for patients in GIM. The primary outcome was non-palliative in-hospital death.
resultsThe study included 13 649 patient admissions in GIM and 8470 patient admissions in subspecialty units. Non-palliative deaths were significantly lower in the intervention period than the pre-intervention period among patients in GIM (1.6% v. 2.1%; adjusted relative risk [RR] 0.74, 95% confidence interval [CI] 0.55-1.00) but not in the subspecialty cohorts (1.9% v. 2.1%; adjusted RR 0.89, 95% CI 0.63-1.28). Among high-risk patients in GIM for whom the system triggered at least 1 alert, the proportion of non-palliative deaths was 7.1% in the intervention period, compared with 10.3% in the pre-intervention period (adjusted RR 0.69, 95% CI 0.46-1.02), with no meaningful difference in subspecialty cohorts (10.4% v. 10.6%; adjusted RR 0.98, 95% CI 0.60-1.59). In the difference-indifferences analysis, the adjusted relative risk reduction for non-palliative death in GIM was 0.79 (95% CI 0.50-1.24).
interpretationImplementing a machine learning-based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning-based early warning systems are promising technologies for improving clinical outcomes.
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