An early warning system for overcrowding in the emergency department

Overcrowding of emergency departments impedes health care access and quality nationwide. A real-time early warning system for overcrowding may allow administrators to alleviate the problem before reaching a crisis state. Two original probabilistic models - a logistic regression and a recurrent neural network - were created to predict overcrowding crises one hour in the future. The two original and two pre-existing models were validated at 8,496 observation points from January 1, 2006 to February 28, 2006. All models showed high discriminatory ability in terms of area under the receiver operating characteristic curve (logistic regression = .954; recurrent neural network = .957; EDWIN = .879; NEDOCS = .924). At comparable rates of false alarms, the logistic regression gave more advance notice of crises than other models (logistic regression = 62 min; recurrent neural network = 13 min; EDWIN = 0 min; NEDOCS = 0 min). These results demonstrate the feasibility of using models based on key operational variables to anticipate overcrowding crises in real time.