ArticleThe American journal of medicine2016
Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.
Article in The American journal of medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2025Article
- Improving Unadjusted and Adjusted Mortality With an Early Warning Sepsis System in the Emergency Department and Inpatient Wards.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2021Article
- Emergency Department Visits and Subsequent Hospital Admission Trends for Patients with Chest Pain and a History of Coronary Artery Disease.Cardiology and therapy · 2020Article
- Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.JAMA network open · 2020Article
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
- A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.Critical care medicine · 2019Article
- Sepsis surveillance: an examination of parameter sensitivity and alert reliability.JAMIA open · 2019Article
- Implementation of an automated early warning scoring system in a surgical ward: Practical use and effects on patient outcomes.PloS one · 2019Article
- An investigation of sepsis surveillance and emergency treatment on patient mortality outcomes: An observational cohort study.JAMIA open · 2018Article
- Article
- Scalable and accurate deep learning with electronic health records.NPJ digital medicine · 2018Article
- A quality improvement project to improve the Medicare and Medicaid Services (CMS) sepsis bundle compliance rate in a large healthcare system.BMJ open quality · 2017Article
- Quick Sequential [Sepsis-Related] Organ Failure Assessment (qSOFA) and St. John Sepsis Surveillance Agent to Detect Patients at Risk of Sepsis: An Observational Cohort Study.American journal of medical quality : the official journal of the American College of Medical QualityObservational
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundReal-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality.
methodsAn automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The systemic inflammatory response syndrome and organ dysfunction alert was applied in real time to 312,214 patients in 24 hospitals and analyzed in 2 phases: training and validation datasets.
resultsIn the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died, whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval, 26.1-34.5; P < .0001). In the validation phase, the sensitivity, specificity, area under the curve, and positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted hazards ratio 4.0; 95% confidence interval, 3.3-4.9; P < .0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P < .0001).
conclusionAn automated alert system that continuously samples electronic medical record data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.
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