title: Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.
abstract: Real-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.
  
  
  
  An 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.
  
  
  
  In 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).
  
  
  
  An 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.
authors.affiliation: Banner TeleHealth, Mesa, Ariz; Care Management, Banner Health, Phoenix, Ariz; Health Management, Banner Health, Phoenix, Ariz. Electronic address: Hargobind.Khurana@bannerhealth.com.
authors.forename: Hargobind S
authors.initials: HS
authors.lastname: Khurana
authors.affiliation: Banner TeleHealth, Mesa, Ariz; Care Management, Banner Health, Phoenix, Ariz; Health Management, Banner Health, Phoenix, Ariz.
authors.forename: Robert H
authors.initials: RH
authors.lastname: Groves
authors.affiliation: Banner Medical Group, Phoenix, Ariz; Banner Estrella Medical Center, Phoenix, Ariz.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Simons
authors.affiliation: Banner Corporate Center, Phoenix, Ariz.
authors.forename: Mary
authors.initials: M
authors.lastname: Martin
authors.affiliation: Information Technology Clinical Systems, Banner Health, Phoenix, Ariz.
authors.forename: Brenda
authors.initials: B
authors.lastname: Stoffer
authors.affiliation: Clinical Performance Analytics, Banner Health, Phoenix, Ariz.
authors.forename: Sherri
authors.initials: S
authors.lastname: Kou
authors.affiliation: University of Arizona College of Medicine-Phoenix.
authors.forename: Richard
authors.initials: R
authors.lastname: Gerkin
authors.affiliation: Banner Research, Banner Health, Phoenix, Ariz; Banner Alzheimer's Institute, Phoenix, Ariz.
authors.forename: Eric
authors.initials: E
authors.lastname: Reiman
authors.affiliation: University of Arizona and Banner University Medical Center, Tucson.
authors.forename: Sairam
authors.initials: S
authors.lastname: Parthasarathy
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D016638:Critical Illness / Q000401:mortality*; D057286:Electronic Health Records; D005260:Female; D005544:Forecasting; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D008875:Middle Aged; D009102:Multiple Organ Failure / Q000175:diagnosis / Q000401:mortality*; D015999:Multivariate Analysis; D016017:Odds Ratio; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D018570:Risk Assessment; D018805:Sepsis / Q000175:diagnosis / Q000401:mortality*; D018746:Systemic Inflammatory Response Syndrome / Q000175:diagnosis / Q000401:mortality*
keywords: Critical illness; Electronic health records; Forecasting; Mortality; Sepsis