Abstract：Objective To predict the outpatient volume of a tertiary general hospital in Zhejiang Province by establishing the seasonal ARIMA model, and to provide a basis for the rational allocation of outpatient human resources.Methods Based on the outpatient visits data of a tertiary general hospital in Zhejiang Province from January 2013 to June 2023, the seasonal ARIMA model was constructed by SPSS software to predict the annual outpatient visits from July 2023 to December 2023. By comparing the measured outpatient visits, the accuracy of the seasonal ARIMA model was evaluated.Results The outpatient volume of the general hospital showed an increasing trend year by year, and showed the characteristics of periodic fluctuations. The optimal seasonal ARIMA model fitted was ARIMA(0,1,1)(1,0,1)12, BIC (Bayesian information criterion) was 5.273, MAPE (mean absolute percentage error) was 14.265, R2 (module determination coefficient) was 0.408, and the overall relative error was 1.83%, indicating good prediction results.Conclusion The seasonal ARIMA model can simulate the change trend of the outpatient volume in the time series of the tertiary general hospital well, and provide a theoretical basis for the short-term forecast of the outpatient volume in the hospital.
［2］TSAN Y T, CHEN D Y, LIU P Y, et al. The prediction of influenza-like illness and respiratory disease using LSTM and ARIMA［J］. Int J Environ Res Public Health, 2022,19(3):1858. DOI: 10.3390/ijerph19031858.
