Real-time estimation of inpatient beds required in emergency departments.

Background: Long boarding time in emergency department (ED) leads to increased morbidity and mortality. Prediction of admissions upon triage could improve ED care efficiency and decrease boarding time.
Objective: To develop a real-time automated model (MA) to predict admissions upon triage and compare this model with triage nurse prediction (TNP).
Patients and Methods: A cross-sectional study was conducted in four EDs during 1 month. MA used only variables available upon triage and included in the national French Electronic Emergency Department Abstract. For each patient, the triage nurse assessed the hospitalization risk on a 10-point Likert scale. Performances of MA and TNP were compared using the area under the receiver operating characteristic curves, the accuracy, and the daily and hourly mean difference between predicted and observed number of admission.
Results: A total of 11 653 patients visited the EDs, and 19.5-24.7% were admitted according to the emergency. The area under the curves (AUCs) of TNP [0.815 (0.805-0.826)] and MA [0.815 (0.805-0.825)] were similar. Across EDs, the AUCs of TNP were significantly different (P < 0.001) in all EDs, whereas AUCs of MA were all similar (P>0.2). Originally, using daily and hourly aggregated data, the percentage of errors concerning the number of predicted admission were 8.7 and 34.4%, respectively, for MA and 9.9 and 35.4%, respectively, for TNP.
Conclusion: A simple model using variables available in all EDs in France performed well to predict admission upon triage. However, when analyzed at an hourly level, it overestimated the number of inpatient beds needed by a third. More research is needed to define adequate use of these models.