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.

Objective To forecast the national nursing human resources situation and provide policy basis for promoting the development of the nurse team.Methods The registered nurse numbers,the total population,the registered(assistant)physician numbers,and the bed numbers in medical and health institutions(in thousands)were selected from 2013 to 2023,and the bed-to-nurse ratio,doctor-to-nurse ratio,and the number of nurses per thousand population were calculated and analyzed to study the changes in national nursing human resources over the past decade.A grey GM(1,1)model was established to predict the number of nurses per thousand population from 2024 to 2030.Results ① The number of nurses per thousand population has increased year by year in the past decade,with an average annual growth rate of 11.38％;② The precision of the grey GM(1,1)model for the number of nurses per thousand population is precise(α=-0.065 9,b=2.014 1,C value=0.003 3,P=1.000),with high fitting degree.And the predicted number of registered nurses per thousand population from 2024 to 2030 are 4.291,4.584,4.896,5.229,5.585,5.965,and 6.371 respectively.Conclusion The national nursing human resources allocation has been optimized in the past decade,and the GM(1,1)model predicts that the national nursing human resources change is also in an upward trend.However,relevant policies still need to be formulated to improve the bed-to-nurse ratio and doctor-to-nurse ratio.

Objective To forecast the national nursing human resources situation and provide policy basis for promoting the development of the nurse team.Methods The registered nurse numbers,the total population,the registered(assistant)physician numbers,and the bed numbers in medical and health institutions(in thousands)were selected from 2013 to 2023,and the bed-to-nurse ratio,doctor-to-nurse ratio,and the number of nurses per thousand population were calculated and analyzed to study the changes in national nursing human resources over the past decade.A grey GM(1,1)model was established to predict the number of nurses per thousand population from 2024 to 2030.Results ① The number of nurses per thousand population has increased year by year in the past decade,with an average annual growth rate of 11.38％;② The precision of the grey GM(1,1)model for the number of nurses per thousand population is precise(α=-0.065 9,b=2.014 1,C value=0.003 3,P=1.000),with high fitting degree.And the predicted number of registered nurses per thousand population from 2024 to 2030 are 4.291,4.584,4.896,5.229,5.585,5.965,and 6.371 respectively.Conclusion The national nursing human resources allocation has been optimized in the past decade,and the GM(1,1)model predicts that the national nursing human resources change is also in an upward trend.However,relevant policies still need to be formulated to improve the bed-to-nurse ratio and doctor-to-nurse ratio.