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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.

SUBMITTER: Khurana HS

PROVIDER: S-EPMC4916370 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.

Khurana Hargobind S HS Groves Robert H RH Simons Michael P MP Martin Mary M Stoffer Brenda B Kou Sherri S Gerkin Richard R Reiman Eric E Parthasarathy Sairam S

The American journal of medicine 20160324 7

<h4>Background</h4>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.<h4>Methods</h4>An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory resp ...[more]

PMID: 27019043

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Project description:ObjectivesMedical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time.MethodsThis study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps.ResultsWe verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented).ConclusionsExperimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement.

Dataset Information

Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.

Publications

Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.

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