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The American Journal of Managed Care November 2018
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Predicting 30-Day Emergency Department Revisits
Kelly Gao; Gene Pellerin, MD; and Laurence Kaminsky, PhD
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Predicting 30-Day Emergency Department Revisits

Kelly Gao; Gene Pellerin, MD; and Laurence Kaminsky, PhD
“Frequent flyers” significantly contribute to emergency department (ED) crowding. This study developed a predictive model that can be used to identify high-risk patients and reduce ED revisits.
ABSTRACT

Objectives: To develop a predictive model that hospitals or healthcare systems can use to identify patients at high risk of revisiting the emergency department (ED) within 30 days and thus reduce unnecessary ED use through proactive interventions.

Study Design: A retrospective analysis of fiscal years (FYs) 2013 and 2014 data from 4 Veterans Affairs hospitals in upstate New York.

Methods: This study developed a predictive model based on administrative data, a publicly available patient classification system, and logistic regression. The study data were from 4 Veterans Affairs hospitals in upstate New York; FY 2013 data were used to predict 30-day revisits in FY 2014. All 22,734 patients with ED visits were included in the analysis. The predictive variables were patient demographics, prior-year utilization, and comorbidities. To prevent overfitting, we validated the model by the split-sample method. The predictive power of the model is measured by C statistics.

Results: In the first model using only patient demographics, the C statistics were 0.568 (95% CI, 0.555-0.580) and 0.556 (95% CI, 0.543-0.568) for the development and validation samples, respectively. In the second model with prior-year utilization added, the C statistics were 0.748 (95% CI, 0.737-0.759) for both samples. In the final model with comorbidities added, the C statistics reached 0.773 (95% CI, 0.762-0.784) and 0.763 (95% CI, 0.753-0.774) for the development and validation samples, respectively.

Conclusions: The predictive model we developed in this study is straightforward to implement and offers significantly higher predictive power than other models reported in the literature. Hospitals and healthcare systems can use it to identify high-risk “frequent flyers” for early interventions to reduce ED revisits.

Am J Manag Care. 2018;24(11):e358-e364

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