ArticleJournal of medical Internet research2019
A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 6 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 6 syntheses or guidelines pooled 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
- 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
- Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital.Applied clinical informatics · 2022Pooled it
- Machine learning techniques for mortality prediction in emergency departments: a systematic review.BMJ open · 2021Pooled it
- Pooled it
- Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials.BMJ (Clinical research ed.) · 2020Pooled it
- Machine learning risk stratification for high-risk infant follow-up of term and late preterm infants.Pediatric research · 2026Article
- Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.NPJ digital medicine · 2026Article
- Simultaneous forecasting of vital sign trajectories in the ICU.Scientific reports · 2025Article
- Bibliometric analysis of scientific research on early obstetric warning system.Journal of education and health promotion · 2025Review
- Effect of predictive nursing based on risk early warning system on patients with acute respiratory failure in Intensive Care Unit.Pakistan journal of medical sciences · 2024Article
- DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach.JMIR medical informatics · 2023Article
- Prediction and analysis of time series data based on granular computing.Frontiers in computational neuroscience · 2023Article
- Improving In-Hospital Patient Rescue: What Are Studies on Early Warning Scores Missing? A Scoping Review.Critical care explorations · 2022Article
- Developing machine learning models to personalize care levels among emergency room patients for hospital admission.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting.Journal of medical Internet research · 2021Review
- Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.
objectiveThe aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.
methodsData were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.
resultsThe EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.
conclusionsIn this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.
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