To develop a predictive model that hospitals or healthcare systems can use to identify patients at high risk of revisiting the ED within 72h so that appropriate interventions can be delivered.

This study employed multivariate logistic regression in developing the predictive model. The study data were from four Veterans medical centers in Upstate New York; 21,141 patients in total with ED visits were included in the analysis. Fiscal Year (FY) 2013 data were used to predict revisits in FY 2014. The predictive variables were patient demographics, prior year healthcare utilizations, and comorbidities. To avoid overfitting, we validated the model by the split-sample method. The predictive power of the model is measured by c-statistic.

In the first model using only patient demographics, the c-statistics were 0.55 (CI: 0.52–0.57) and 0.54 (95% CI: 0.51–0.56) for the development and validation samples, respectively. In the second model with prior year utilization added, the c-statistics were 0.70 (95% CI: 0.68–0.72) for both samples. In the final model where comorbidities were added, the c-statistics were 0.74 (CI: 0.72–0.76) and 0.73 (95% CI: 0.71–0.75) for the development and validation samples, respectively.

Reducing ED revisits not only lowers healthcare cost but also shortens wait time for those who critically need ED care. However, broad intervention for every ED visitor is not feasible given limited resources. In this study, we developed a predictive model that hospitals and healthcare systems can use to identify “frequent flyers” for early interventions to reduce ED revisits.