Research on the Prediction of Health Human Resources in Tianjin Based on Three Models
Objective Through rational allocation of health resources,the public can get basic medical and health services,and the equity of health resources can be better reflected.Based on the number of health human resources in Tianjin from 1997 to 2021,the number of health personnel in the next few years is predicted to provide policy basis for Tianjin to train and introduce high-quality health talents.Methods The number of all health personnel,practicing(practicing assistant)physicians,registered nurses,licensed(licensed assistants)pharmacists and medical technologists(medical technicians)in Tianjin health institutions from 1997 to 2021 were obtained from public sources.The grey GM(1,1)model,autoregressive integrated moving average(ARIMA)model and exponential smoothing model were used to fit the data.The absolute and average error values were used to compare the advantages and disadvantages of different models,and the optimal model was selected to predict the number of health human resources in Tianjin from 2022 to 2026.Results The exponential smoothing model was superior to the grey GM(1,1)model and the ARIMA prediction model,and was more suitable for human resource prediction.The preset target for 2025 was 52 500 practicing(practicing assistant)physicians and 60 000 registered nurses.Compared with the forecast results,the number of physicians would exceed the expected target,while the number of nurses would be slightly short.It was expected that Tianjin would basically complete the forecast target in 2026.Conclusion Government departments,medical colleges and the whole society should work together to improve the policy for medical talents and enhance the attractiveness of hospitals to talents,so as to cultivate and introduce health talents on a large scale,and promote the high-quality development of health undertakings in Tianjin.
Tianjinhealth human resources forecastinggrey GM(1,1)modelautoregressive integrated moving average modelexponential smoothing modelpredictive research
万方数据
维普
