The best-fit models for P3 and total attendances were ARIMA(0,1,1)(1,0,1), which are seasonal non-stationary moving average model. Table 4 Best-fit ARIMA models and their predictors by patient acuity category All the four data series had linear trend since all ‘d’s in the best-fit models equal 1. P1 attendance did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities in the time series analyses, and were also significantly correlated with SB939 public holiday. P3 attendance was significantly correlated with day of the week, month of the year,

public holiday, and ambient air quality of PSI > 50. The maximum Inhibitors,research,lifescience,medical lag between PSI

> 50 and P1 cases was two days; there was no lag between PSI > 50 and P3 cases. The maximum lag between public Inhibitors,research,lifescience,medical holiday and P2, P3 and total cases was one day (Table ​(Table44). P1 yielded a MAPE of 16.9% on validation; or forecasts of the model had an average error of 6 out of an average 33 attendances per day. The models for P2, P3 and total attendances performed better in the daily prediction of attendances, with a MAPE of 6.7%, 8.6% and 4.8%, respectively. Fig. ​Fig.44 shows the observed and predicted time series for P1, P2, P3 and total attendances overlap with each other to Inhibitors,research,lifescience,medical a great degree. The scatter plots of observed vs predicted attendances by the four best-fit models shows that the points to be distributed along the diagonal line (Fig. ​(Fig.5);5); i.e. the models were successful in accounting for most of the significant autocorrelations present in the data. Figure 4 Observed and predicted daily attendances at emergency department by patient acuity categories, Jul 2007–Mar 2008. Inhibitors,research,lifescience,medical Figure 5 Scatter plot of numbers of daily attendances at emergency department by patient acuity categories, observed vs predicted, Jul 2007 – Mar 2008. Discussion Although emergencies are difficult to foresee, this study demonstrated that daily patient attendances at ED

can be predicted with good accuracy Inhibitors,research,lifescience,medical using the modeling techniques in time series analysis. During the study period, the aminophylline daily variations noted were quite significant, with daily P1 attendances ranging from 10 to 72; P2 attendances ranging from 96 to 239; P3 attendances ranging from 138 to 307. The model developed has identified factors associated with these variations in a local setting; which in turn were used to forecast future workload. Although the P1 model showed the highest prediction error due to the very small number of daily P1 attendances, it still demonstrated good forecasting ability. Unlike other studies [6,8], this study showed that daily total ED attendances were not predicted by weather conditions. This could be because Singapore is a tropical city with little variation in its hot and humid weather conditions throughout the year.