A multi-method patient arrival forecasting outline for hospital emergency departments

ABSTRACT

Patient arrivals at the emergency department (ED) of hospitals has an unpredictable behavior. So that, adequate forecasting of this process can serve a management baseline to better allocate ED human resources and medical equipment. In this paper, a multi-method patient arrival forecasting outline for EDs is developed. The methods followed within this study include single methods as linear regression (LR), autoregressive integrated moving average (ARIMA), artificial neural network (ANN), exponential smoothing and hybrid methods as ARIMA–ANN and ARIMA-LR. As the subject of the study, a private hospital ED case in Turkey is carried out. Data of ED patient arrivals for the year of 2016 was used to set up models. Forecasting performance of the multi-method outline was measured using mean absolute percentage error. The ARIMA–ANN hybrid model is shown to outperform in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying these methods to model ED patient arrivals and making an overall assessment among them. The results can be used to aid in strategic decision-making on ED staffing and scheduling policy planning in response to predictable arrival variations.

KEYWORDS:

• Forecasting
• emergency departments
• patient arrival
• ARIMA
• ANN
• hybrid model

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Melih Yucesan (PhD) has been working as Assistant Professor at the Department of Mechanical Engineering, Munzur University, Tunceli, Turkey. His research interests are production planning, forecasting and fuzzy sets. A recent paper was appeared in Drvna industrija about sales forecasting of furniture products by artificial neural network modeling.

Muhammet Gul (PhD) has been working as Assistant Professor at the Department of Industrial Engineering, Munzur University, Tunceli, Turkey. His research interests are simulation modelling, healthcare management, occupational safety and fuzzy sets. His papers appeared in international journals such as Computers & Industrial Engineering, Knowledge-Based Systems, Applied Soft Computing.

Erkan Celik (PhD) has been working as Associate Professor at the Department of Industrial Engineering, Munzur University, Tunceli, Turkey. His papers appeared in international journals such as Transportation Research Part:E, Computers & Industrial Engineering and International Journal of Production Research. His research interests are decision analysis, humanitarian logistic, fuzzy sets.