Abstract：Objective To predict the regional nursing and health human resources with the use of GM (1,1) model by taking the prediction of the number of nurses per thousand permanent residents as an example, in order to provide decision support for the health management department.Methods The number of registered nurses per thousand resident population in Shao-xing from 2011 to 2020 was collected, and the GM(1,1) model was used to predict and analyze the demand for regional nurses.Results The number of nurses per thousand resident population in the region predicted by GM (1,1) model showed an upward trend in the next three years. The fitting error between the predicted value and the actual value was small, and the prediction accuracy was excellent (C=0.231, α=-0.066). According to the model, the number of registered nurses per 1 000 resident population in Shaoxing predicted from 2021 to 2023 will reach 3.72, 3.97, and 4.25, respectively.Conclusion The GM(1,1) model can fit the demand changes for regional nursing human resources in time series data, and can provide a basis for scientific and rational allocation of regional health human resources.
张春霞，阮伟良，林建潮. 基于GM（1，1）模型的绍兴市“十四五”期间护理人力资源预测研究[J]. 中国医院统计, 2022, 29(2): 140-143.
Zhang Chunxia, Ruan Weiliang, Lin Jianchao. Research on nursing human resource prediction based on GM (1,1) model during the 14th Five-Year Plan Period in Shaoxing. journal1, 2022, 29(2): 140-143.
