Farid Kadri

Time Series Modelling and Forecasting of Emergency Department Overcrowding

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

Improved principal component analysis for anomaly detection

Developed PCA-based MCUSUM anomaly detection (AD) method.Extended the AD advantages of the MCUSUM to enhance the conventional PCA.The proposed algorithm is applied to monitor an emergency department.The detection results show effectiveness of the proposed method. Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotellings T 2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

Seasonal ARMA-based SPC charts for anomaly detection

Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

Early detection of abnormal patient arrivals at hospital emergency department

Overcrowding is one of the most crucial issues confronting emergency departments (EDs) throughout the world. Efficient management of patient flows for ED services has become an urgent issue for most hospital administrations. Handling and detection of abnormal situations is a key challenge in EDs. Thus, the early detection of abnormal patient arrivals at EDs plays an important role from the point of view of improving management of the inspected EDs. It allows the EDs mangers to prepare for high levels of care activities, to optimize the internal resources and to predict enough hospitalization capacity in downstream care services. This study reports the development of statistical method for enhancing detection of abnormal daily patient arrivals at the ED, which able to provide early alert mechanisms in the event of abnormal situations. The autoregressive moving average (ARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the pediatric emergency department (PED) at Lille regional hospital center, France.

Resilience-based performance assessment of strain situations in emergency departments

In response to events and exceptional situations (e.g. health threats related to epidemics, seasonal flux, heat waves, and cold waves), hospital establishments in particular emergency departments, must be able to receive patients for medical and surgical treatments whatever the extent of the patient flow. The conventional medical resources are often outdated and ineffective in absorbing a large influx of patients, which often leads to strain situations. Hence it has become essential to strengthen the organization of hospital emergency departments so they can manage such situations. To cope with such situations, emergency departments must incorporate in their operating mode the capacity to anticipate, to react and to mobilize the necessary resources in order to have a sufficient level of resilience to meet their missions. In this paper we define and characterize the resilience of an emergency department, and propose a generic procedure to evaluate the resilience of the emergency departments.

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.