Enhanced monitoring of abnormal emergency department demands

This paper presents a statistical technique for detecting signs of abnormal situation generated by the influx of patients at emergency department (ED). The monitoring strategy developed was able to provide early alert mechanisms in the event of abnormal situations caused by abnormal patient arrivals to the ED. More specifically, This work proposed the application of autoregressive moving average (ARMA) models combined with the generalized likelihood ratio (GLR) test for anomaly-detection. ARMA was used as the modelling framework of the ARMA-based GLR anomaly-detection methodology. The GLR test was applied to the uncorrelated residuals obtained from the ARMA model to detect anomalies when the data did not fit the reference ARMA model. The ARMA-based GLR hypothesis testing scheme was successfully applied to the practical data collected from the database of the pediatric emergency department (PED) at Lille regional hospital center, France.

[1]  Stephen E. Fienberg,et al.  Testing Statistical Hypotheses , 2005 .

[2]  K. Yürekli,et al.  Testing the Residuals of an ARIMA Model on the Çekerek Stream Watershed in Turkey , 2005 .

[3]  Fouzi Harrou,et al.  Anomaly detection/detectability for a linear model with a bounded nuisance parameter , 2014, Annu. Rev. Control..

[4]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[5]  Damien Trentesaux,et al.  Modelling and management of strain situations in hospital systems using an ORCA approach , 2013, Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM).

[6]  L. Pazvakawambwa,et al.  Forecasting methods and applications. , 2013 .

[7]  Arthur L Kellermann,et al.  Crisis in the emergency department. , 2006, The New England journal of medicine.

[8]  Robert Champion,et al.  Forecasting emergency department presentations. , 2007, Australian health review : a publication of the Australian Hospital Association.

[9]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[10]  Hazem Nounou,et al.  Statistical fault detection using PCA-based GLR hypothesis testing , 2013 .

[11]  David E. Booth.  Time Series: Forecasting, Simulation, Applications , 1993 .

[12]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[13]  E. Seow,et al.  Forecasting daily attendances at an emergency department to aid resource planning , 2009, BMC emergency medicine.

[14]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[15]  Farid Kadri,et al.  Improved principal component analysis for anomaly detection: Application to an emergency department , 2015, Comput. Ind. Eng..

[16]  Shari J. Welch,et al.  Forecasting daily patient volumes in the emergency department. , 2008, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[17]  Robert H. Shumway,et al.  Time series analysis and its applications : with R examples , 2017 .

[18]  Farid Kadri,et al.  Time Series Modelling and Forecasting of Emergency Department Overcrowding , 2014, Journal of Medical Systems.

[19]  Farid Kadri,et al.  A simulation-based decision support system to prevent and predict strain situations in emergency department systems , 2014, Simul. Model. Pract. Theory.