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| Study on the prediction of health human resources in Shenzhen based on the grey regression coupling model |
| Zhou Chaohua1, Xia Xiaoqiong2, Wu Xiaoyun3, Peng Cheng4, Ye Xiufeng5 |
1 Equipment Department, Shenzhen People′s Hospital, Shenzhen 518020, China;
2 CPC Committee Office, Shenzhen People′s Hospital, Shenzhen 518020, China;
3 Operation Management Department, Shenzhen People′s Hospital, Shenzhen 518020, China;
4 Human Resource Department, Shenzhen People′s Hospital, Shenzhen 518020, China;
5 Administration Office, Shenzhen People′s Hospital, Shenzhen 518020, China |
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Abstract Objective To predict the current situation of the allocation of health human resources in Shenzhen with the use of the grey regression coupling model, and to provide reference for the formulation of relevant health policies. Methods SPSS 19.0 statistical analysis software was used to construct a linear regression model between the number of practicing (assistant) physicians per thousand population and the number of registered nurses per thousand population and influencing factors. Excel formula programming was used to establish a grey prediction model to predict the influencing factors, and finally the predicted value was substituted into the linear regression model for coupling prediction. Results 2019, the number of practicing (assistant) physicians per thousand population was 3.01, an increase of 46.11% over 2007, with an average annual growth rate 3.21%; the number of registered nurses per thousand population was 3.30, an increase of 68.37% over 2007, with an average annual growth rate 4.44%. The accuracy of the grey prediction model was level 1 (excellent), and the number of practicing (assistant) physicians per thousand population from 2020 to 2025 was 3.05, 3.23, 3.44, 3.68, 3.96, and 4.19, and the number of nurses per thousand population from 2020 to 2025 was 3.49, 3.70, 3.92, 4.18, 4.46, and 4.73 respectively. Conclusion From 2020 to 2025, the required number of health human resources in Shenzhen would show an increasing trend. This prediction result could provide a scientific reference for the allocation of health human resources.
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Received: 31 March 2021
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