Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era
Rational hospital human resource allocation planning is important to improve the efficiency of China’s health human resource allocation and reduce the losses caused by staff waste and shortage. In this paper, we take the medical and nursing configuration of a general tertiary hospital in X city as a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00534 |
| _version_ | 1797340732340568064 |
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| author | Jiao Haiyan Li Jiangbo Liu Lin Zhao Haibo |
| author_facet | Jiao Haiyan Li Jiangbo Liu Lin Zhao Haibo |
| author_sort | Jiao Haiyan |
| collection | DOAJ |
| description | Rational hospital human resource allocation planning is important to improve the efficiency of China’s health human resource allocation and reduce the losses caused by staff waste and shortage. In this paper, we take the medical and nursing configuration of a general tertiary hospital in X city as a guiding framework and use inductive and deductive methods to summarize the factors affecting medical and nursing staffing and the experience of management in the previous period. By proposing an adaptive algorithm based on learning rate for improving BP neural network with differentiated learning rate, the dynamic adjustment of weights between different nodes is achieved. Finally, through database design and module design, two functional modules of human resource management and human resource prediction are constructed. The results of the case validation show that the HR demand forecasting model has the best prediction effect for health technicians, and the relative errors are all less than 5%, with an average relative error of 1.23% and a minimum value of only 0.25%. The relative error between the predicted and actual values of the ARIMA (2, 2, 2) dataset for practicing (assistant) physicians is less than 0.005. It shows that the HR model constructed in this paper has a certain quantitative guidance value for the rational planning of human resource allocation for hospital positions. |
| first_indexed | 2024-03-08T10:07:31Z |
| format | Article |
| id | doaj.art-30aa0bc2ed164b318bef299751106fe0 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:07:31Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-30aa0bc2ed164b318bef299751106fe02024-01-29T08:52:33ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00534Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic EraJiao Haiyan0Li Jiangbo1Liu Lin2Zhao Haibo31Shenzhen Dept of Human Resource, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, 518057, China.1Shenzhen Dept of Human Resource, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, 518057, China.1Shenzhen Dept of Human Resource, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, 518057, China.2Department of Dermatology, Shenzhen People’s Hospital, Shenzhen, Guangdong, 518057, China.Rational hospital human resource allocation planning is important to improve the efficiency of China’s health human resource allocation and reduce the losses caused by staff waste and shortage. In this paper, we take the medical and nursing configuration of a general tertiary hospital in X city as a guiding framework and use inductive and deductive methods to summarize the factors affecting medical and nursing staffing and the experience of management in the previous period. By proposing an adaptive algorithm based on learning rate for improving BP neural network with differentiated learning rate, the dynamic adjustment of weights between different nodes is achieved. Finally, through database design and module design, two functional modules of human resource management and human resource prediction are constructed. The results of the case validation show that the HR demand forecasting model has the best prediction effect for health technicians, and the relative errors are all less than 5%, with an average relative error of 1.23% and a minimum value of only 0.25%. The relative error between the predicted and actual values of the ARIMA (2, 2, 2) dataset for practicing (assistant) physicians is less than 0.005. It shows that the HR model constructed in this paper has a certain quantitative guidance value for the rational planning of human resource allocation for hospital positions.https://doi.org/10.2478/amns.2023.2.00534hospital human resourceslearning ratebp neural networkdemand forecasting model62p20 |
| spellingShingle | Jiao Haiyan Li Jiangbo Liu Lin Zhao Haibo Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era Applied Mathematics and Nonlinear Sciences hospital human resources learning rate bp neural network demand forecasting model 62p20 |
| title | Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era |
| title_full | Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era |
| title_fullStr | Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era |
| title_full_unstemmed | Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era |
| title_short | Study on the Rationalization of Human Resources Allocation in Hospitals in the Post-Epidemic Era |
| title_sort | study on the rationalization of human resources allocation in hospitals in the post epidemic era |
| topic | hospital human resources learning rate bp neural network demand forecasting model 62p20 |
| url | https://doi.org/10.2478/amns.2023.2.00534 |
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