Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department

Emergency departments (EDs) in the United States are overcrowded and nearing a breaking point. Alongside ever-increasing demand, one of the leading causes of ED overcrowding is the boarding of hospitalized patients in the ED as they await bed placement. We sought to develop a model for early prediction of hospitalizations, thus enabling an earlier start for the placement process and shorter boarding times.

We conducted a retrospective cohort analysis of all visits to the Boston Children's Hospital ED from July 1, 2014 to June 30, 2015. We used 50% of the data for model derivation and the remaining 50% for validation. We built the predictive model by using a mixed method approach, running a logistic regression model on results generated by a naive Bayes classifier. We performed sensitivity analyses to evaluate the impact of the model on overall resource utilization.

Our analysis comprised 59 033 patient visits, of which 11 975 were hospitalized (cases) and 47 058 were discharged (controls). Using data available within the first 30 minutes from presentation, our model identified 73.4% of the hospitalizations with 90% specificity and 35.4% of hospitalizations with 99.5% specificity (area under the curve = 0.91). Applying this model in a real-time setting could potentially save the ED 5917 hours per year or 30 minutes per hospitalization.

This approach can accurately predict patient hospitalization early in the ED encounter by using data commonly available in most electronic medical records. Such early identification can be used to advance patient placement processes and shorten ED boarding times.