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Real-time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.
Am J Med. 2016 Mar 23;
Authors: Khurana HS, Groves RH, Simons MP, Martin M, Stoffer B, Kou S, Gerkin R, Reiman E, Parthasarathy S
Abstract
BACKGROUND: 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.
METHODS: An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least two of four systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The SIRS/OD alert was applied real-time to 312,214 patients in 24 hospitals and analyzed in two phases: training and validation datasets.
RESULTS: 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 [95%CI] 26.1, 34.5; P<0.0001). In the validation phase, the sensitivity, specificity, area under curve (AUC), 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%CI 3.3, 4.9; P<0.0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P<0.0001).
CONCLUSION: 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.
PMID: 27019043 [PubMed - as supplied by publisher]