Prediction of women and Children's hospital outpatient numbers based on the autoregressive integrated moving average model

Objective: To evaluate the predictive value of the autoregressive integrated moving average (ARIMA) product seasonal model for the daily outpatient volume of paediatric internal medicine departments in hospitals.Methods: The daily outpatient volume of paediatric internal medicine recorded by the hospital information system of the Chengdu Women's and Children's Central Hospital from 1 January 2011 to 31 December 2020 was collected. Using the data from 1 January 2011 to 31 December 2019, the seasonal summation ARIMA model of the time product was established by fitting the tseries program in the R-3.6.3 software. The monthly outpatient volume from January to December 2020 was predicted, and the prediction effect was evaluated according to the mean absolute percentage error (MAPE) between the predicted value and the actual value. Results: The outpatient volume of paediatric internal medicine in the hospital from 2011 to 2019 showed an upward trend, with obvious seasonal fluctuations. The optimal model was the ARIMA model ([3,4], 1,2) x (1,1,0) 12, with an Akaike information criterion of 3116.656 and a Bayesian information criterion of 3217.412. The model's residual was a white noise sequence (x2 = 7.56, P = 0.819), and the MAPE between the predicted value and the actual value of the model was 9.56%. Within a 95% confidence interval of the predicted value, the prediction accuracy of the model was high. Conclusion: The ARIMA multiplicative seasonal model established in this study is suitable for the short-term prediction of the outpatient volume.