Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network

Xu, M. and Wong, T.C. and Chin, K.S. (2013) Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network. Decision Support Systems, 54 (3). pp. 1488-1498. ISSN 0167-9236

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Abstract

Emergency Department (ED) plays a critical role in healthcare systems by providing emergency care to patients in need. The quality of ED services, measured by waiting time and length of stay, is significantly affected by patient arrivals. Increased patient arrivals could undermine service timeliness, thus putting patients in severe conditions at risk. These factors lead to the following research questions that have rarely been studied before: What are the variables directly associated with patient arrivals in the ED? What is the nature of association between these variables and patient arrivals? Which variable is the most influential and why? To address the above questions, we proposed a three-stage method in this paper. First, a data-driven method is used to identify contributing variables directly correlated with the daily arrivals of Categories 3 and 4 patients (i.e., non-critical patients). Second, the association between contributing variables and daily patient arrival is modeled by using artificial neural network (ANN), and the modeling ability is compared with that of nonlinear least square regression (NLLSR) and multiple linear regression (MLR) in terms of mean average percentage error (MAPE). Third, four types of relative importance (RI) of input variables based on ANN are compared, and their statistical reliability is tested by the MLR-based RI. We applied this three-stage method to one year of data of patient arrivals at a local ED. The contribution of this paper is twofold. Theoretically, this paper emphasizes the importance of using data-driven selection of variables for complex system modeling, and then provides a comprehensive comparison of RI using different computational methods. Practically, this work is a novel attempt of applying ANN to model patient arrivals, and the result can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations.