ArticleJAMA network open2024
Early Warning Scores With and Without Artificial Intelligence.
Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 papers, 4 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
47 citing papers in PubMed, 4 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
- A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools.Journal of general internal medicine · 2026Pooled it
- Ethical challenges in the algorithmic era: a systematic rapid review of risk insights and governance pathways for nursing predictive analytics and early warning systems.BMC medical ethics · 2025Pooled it
- Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- The Double-Edged Sword Effect of AI Application Among Clinical Nurses Based on the Job Demands-Resources Model: Qualitative Study.JMIR nursing · 2026Article
- When the algorithm passes the test but fails the patient.PLOS digital health · 2026Article
- Do bots provide correct and adequate guidance regarding acidity: A blinded comparison rated by patients and physicians.World journal of methodology · 2026Article
- Decision-centered artificial intelligence for perioperative care outside the operating room: a practical review for surgeons.Journal of minimally invasive surgery · 2026Review
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- Article
- DETERIO-LLM: enhancing traditional deterioration risk scores with clinical context and advanced reasoning.JAMIA open · 2026Article
- No More False Alert: Contrastive Learning for Predicting Health Deterioration from Imbalanced Care Records.Sensors (Basel, Switzerland) · 2026Article
- Prediction of 48-h Intensive Care Unit Mortality in Hospitalized Cancer Patients: Performance of the EPIC Deterioration Index and Sequential Organ Failure Assessment Score.Journal of intensive care medicine · 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
- Advantages and challenges for utilization of generative artificial intelligence in clinical nursing practice: an integrative review.BMC nursing · 2026Article
- Early Warning Scores in Emergency Department Patients Aged 80 Years or Older.JAMA network open · 2026Article
- Wolf Creek XVIII Part 5: cardiac arrest survivorship science.Resuscitation plus · 2026Article
- One Score Fits All? A Narrative Review on Early Warning Scores for Older Adults in the Emergency Department in the Era of Personalized Medicine.Journal of personalized medicine · 2026Review
- 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
- Artificial Intelligence (AI) driven patient assignment: optimizing daily redistribution among hospitalists.Frontiers in medicine · 2026Article
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
Importance: Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance. Objective: To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores. Design, Setting, and Participants: This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System. All consecutive adult medical-surgical ward hospital encounters between March 9, 2019, and November 9, 2023, were included. Exposures: Simultaneous Epic Deterioration Index (EDI), Rothman Index (RI), eCARTv5 (eCART), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and NEWS2 scores. Main Outcomes and Measures: Clinical deterioration, defined as a transfer from ward to intensive care unit or death within 24 hours of an observation. Results: Of the 362 926 patient encounters (median patient age, 64 [IQR, 47-77] years; 200 642 [55.3%] female), 16 693 (4.6%) experienced a clinical deterioration event. eCART had the highest area under the receiver operating characteristic curve at 0.895 (95% CI, 0.891-0.900), followed by NEWS2 at 0.831 (95% CI, 0.826-0.836), NEWS at 0.829 (95% CI, 0.824-0.835), RI at 0.828 (95% CI, 0.823-0.834), EDI at 0.808 (95% CI, 0.802-0.812), and MEWS at 0.757 (95% CI, 0.750-0.764). After matching scores at the moderate-risk sensitivity level for a NEWS score of 5, overall positive predictive values (PPVs) ranged from a low of 6.3% (95% CI, 6.1%-6.4%) for an EDI score of 41 to a high of 17.3% (95% CI, 16.9%-17.8%) for an eCART score of 94. Matching scores at the high-risk specificity of a NEWS score of 7 yielded overall PPVs ranging from a low of 14.5% (95% CI, 14.0%-15.2%) for an EDI score of 54 to a high of 23.3% (95% CI, 22.7%-24.2%) for an eCART score of 97. The moderate-risk thresholds provided a median of at least 20 hours of lead time for all the scores. Median lead time at the high-risk threshold was 11 (IQR, 0-69) hours for eCART, 8 (IQR, 0-63) hours for NEWS, 6 (IQR, 0-62) hours for NEWS2, 5 (IQR, 0-56) hours for MEWS, 1 (IQR, 0-39) hour for EDI, and 0 (IQR, 0-42) hours for RI. Conclusions and Relevance: In this cohort study of inpatient encounters, eCART outperformed the other AI and non-AI scores, identifying more deteriorating patients with fewer false alarms and sufficient time to intervene. NEWS, a non-AI, publicly available early warning score, significantly outperformed EDI. Given the wide variation in accuracy, additional transparency and oversight of early warning tools may be warranted.
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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.