Comput Math Methods Med - Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model.

Tópicos

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Resumo

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.

Resumo Limpo

studi analyz meteorolog clinic econom factor term effect month ed revenu visitor volum month data januari septemb analyz spearman correl crosscorrel analys perform identifi correl independ variabl ed revenu visitor volum autoregress integr move averag arima model use quantifi relationship independ variabl ed revenu visitor volum accuraci evalu compar model forecast actual valu mean absolut percentag error sensit predict error model train time also evalu arima model indic mean maximum temperatur relat humid rainfal nontrauma trauma visit may correl posit ed revenu mean minimum temperatur may correl negat ed revenu moreov mean minimum temperatur stock market index fluctuat may correl posit trauma visitor volum mean maximum temperatur relat humid stock market index fluctuat may correl posit nontrauma visitor volum mean maximum temperatur relat humid may correl posit pediatr visitor volum mean minimum temperatur may correl negat pediatr visitor volum model also perform well forecast revenu visitor volum

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