Monitoring patient flow in a hospital emergency department: ARMA-based nonparametric GLRT scheme

Abstract

Overcrowding in emergency departments (EDs) is a primary concern for hospital administration. They aim to efficiently manage patient demands and reducing stress in the ED. Detection of abnormal ED demands (patient flows) in hospital systems aids ED managers to obtain appropriate decisions by optimally allocating the available resources following patient attendance. This paper presents a monitoring strategy that provides an early alert in an ED when an abnormally high patient influx occurs. Anomaly detection using this strategy involves the amalgamation of autoregressive-moving-average (ARMA) time series models with the generalized likelihood ratio (GLR) chart. A nonparametric procedure based on kernel density estimation is employed to determine the detection threshold of the ARMA-GLR chart. The developed ARMA-based GLR has been validated through practical data from the ED at Lille Hospital, France. Then, the ARMA-based GLR method’s performance was compared to that of other commonly used charts, including a Shewhart chart and an exponentially weighted moving average chart; it proved more accurate.