Hospital outpatient visit analysis and forecasting using time series models

Analysis and forecasting of hospital outpatient visits are important in making correct and feasible decisions for hospital resources management and high quality patient care provision. However, research in outpatient visit analysis and forecasting has not drawn much attentions so far, and current research mainly focuses on the computational methods for forecasting only, lacking in comprehensive analysis, rules finding, and knowledge discovery for hospital outpatient visits. Thus it was propsed to construct autoregressive moving average models (ARMAX), neural network models, and hybrid models integrating ARMAX and NN for outpatient visit analysis and forecasting. By constructing these models, the rules of the daily outpatient visit of the Xiamen city, China were analyzed comprehensively. It was fund that outpatient visit data show a significantly upward time trend, a significant day-of-week effect, and a significant serial autocorrelation. By comparing the forecasting performance of these time series models, it was fund that the ARMAX+NN hybrid model achieves better performance, which is mainly due to the fact that the hybrid model can capture both linear and nonlinear parts of the outpatient visit data.