Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis

Abstract

Two univariate time-series analysis methods have been used to model and forecast the monthly patient volume at the family and community medicine primary health care clinic of King Faisal University, Al-Khobar, Saudi Arabia. Models were based on nine years of data and forecasts made for 2 years. The optimum ARIMA model selected is an autoregressive model of the fourth order operating on the data after differencing twice at the nonseasonal level and once at the seasonal level. It gives mean and maximum absolute percentage errors of 1.86 and 4.23%, respectively, over the forecasting interval. A much simpler method based on extrapolating the growth curve of the annual means of the patient volume using a polynomial fit gives the better figures of 0.55 and 1.17%, respectively. This is due to the fairly regular nature of the data and the lack of strong random components that require ARIMA processes for modeling.