Meiho University Institutional Repository:Item 987654321/1752
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    题名: Long-TermPrediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving AverageModel
    作者: Chieh-Fan Chen;Wen-Hsien Ho;Huei-Yin Chou;Shu-Mei Yang;I-Te Chen;Hon-Yi Shi
    日期: 2011
    上传时间: 2012-09-13T06:36:14Z (UTC)
    摘要: This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor
    volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation
    analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive
    integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable,
    ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute
    percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that
    mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue,
    but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock
    market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity
    and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and
    relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively
    with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.
    關聯: Computational and Mathematical Methods in Medicine Volume 2011
    显示于类别:[健康事業管理系 ] 期刊論文

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