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目的:构建某精神专科医院急诊量预测模型，分析急诊科就诊人次的变化规律，为精神科急诊服务资源的优化配置提供参考。方法:从某精神专科医院信息系统提取2018—2023年急诊患者就诊时间等数据。其中，2018—2022年月度急诊人次(急诊量)用于构建自回归积分滑动平均模型(autoregressive integrated moving average model，ARIMA)，2023年月度急诊量用于验证该模型的预测效果。结果:经模型构建和筛选，确定季节型ARIMA(0，1，0)(1，1，1) 12为最优模型，该模型的预测值与实际值吻合性较好，平均相对误差波动在1.6%～26.8%，平均绝对误差波动在9～159人次。 结论:季节型ARIMA模型能够较准确地预测某精神专科医院急诊量，可为该院人力资源配置及应急调度提供参考，但该预测模型适用于短期预测，如需长期预测，还应不断进行数据拟合，以确保预测的有效性。

Objective:To construct a prediction model for the emergency volume of the psychiatric hospital, and analyze the changes of psychiatric emergency visits, so as to provide references for optimizing the allocation of emergency service resources.Methods:This study extracted data of the visit time of emergency patients, etc from the information system of a certain psychiatric hospital from 2018 to 2023. The monthly emergency visits (emergency volume) from 2018 to 2022 were used to construct the autoregressive integrated moving average model (ARIMA), and the monthly emergency volume from 2023 was used to validate the predictive performance of the model.Results:After model construction and screening, seasonal ARIMA (0, 1, 0) (1, 1, 1) 12 was determined as the optimal model. The predicted values of the model were in good agreement with the actual values, with an average relative error fluctuation of 1.6% to 26.8% and an average absolute error fluctuation of 9 to 159 person-time. Conclusions:The seasonal ARIMA model could accurately predict the emergency volume of a certain psychiatric hospital and provide references for human resource allocation and emergency response. However, this prediction model was suitable for short-term forecasting. If long-term forecasting was needed, continuous data fitting was necessary to ensure the effectiveness of the prediction.