The objective of this study was to find the appropriate forecasting model for monthly

patient admissions and discharges. A data set on monthly patient admissions and discharges was

collected from the Rajavithi Hospital, consisting of 72 monthly observations from October 1998 to

September 2004. The analyses were done in four steps. First, the intervention analysis was conducted

to model the historical data taking into account a new intervention policy of the universal of

healthcare project (UHP), which started on October 1, 2001. Second, the Box-Jenkins (BJ) method

was applied to develop the forecasting models using the past data of 36, 48, 60 and 72 monthly series

that ran backward from September 2004 for each individual series of monthly patient admissions and

discharges. Third, The forecasting performances of the BJ models were evaluated using the mean

absolute deviation (MAD), the mean square error (MSE) and the mean absolute percentage Error

(MAPE). The appropriate forecasting model was considered from the minimum values of these

indices. Fourth, the chosen model was used to make forecast for six months ahead of monthly patient

admissions and discharges. Then, the forecast values were compared with the actual values for the

period of October 2004 to March 2005. Results showed that the BJ approach was a more appropriate

way for both monthly patient admissions and discharges using the 60 monthly historical data.

However, the accuracy of the forecasting depended upon the variation within the data.