JMIR Publications

ABSTRACT

Background:

According to the "National Development Plan for Nursing (2021-2025)" issued by the National Health Commission, promoting high-quality development of the nursing profession is essential for improving the health of the general public. As healthcare providers, practicing nurses are an indispensable part of medical institutions, and are crucial factors in safeguarding patient health and medical safety, as well as driving progress towards achieving health development goals in all countries across the world. However, global healthcare services face enormous challenges such as a shortage of nursing staff and uneven distribution. The number of registered nurses per thousand is the number of registered nurses per 1,000 permanent residents, according to World Health Organization statistics, Norway has the highest per capita number of nurses in the world with 17.27 nurses per thousand people, while the basic standard set by the European Union is 8 nurses per thousand people or more. The United States and Japan have 9.8 and 11.49 nurses per thousand people, respectively. In contrast, China has only 3.56 nurses per thousand people, which is significantly lower than developed countries. In addition, with an aging population and increase in the number of patients with chronic diseases, the demand for medical services in China is growing. Talent demand forecasting is an important part of human resource planning, and plays a decisive role in the development and social stability of various industries. In the medical industry, the supply and demand of nursing talent directly affect the quality and efficiency of medical services. Therefore, predicting and analyzing the demand for future registered nurses provides a scientific basis for relevant institutions and departments. By studying the training and education of nursing talent, improving the quality and ability of nursing talent, better meeting the needs of future positions, and improving the quality and level of nursing services, promoting the overall development of the nursing industry has important practical significance. The Grey GM (1,1) model is a widely used forecasting model that was proposed by Professor Deng Julong in the 1980s. The core idea is to make predictions within the unknown range of the required data using a small amount of available data. This model is known for its high predictive accuracy and effectiveness. The current literature has not yet developed the application of this model for forecasting nursing workforce. In this study, we applied the gray GM (1,1) model to forecast the demand for nursing positions in China over the next 10 years. Additionally, we analyzed the forecast results in conjunction with current trends in social development.

Objective:

The research object was registered nursing talent resources per thousand people in China. Data were extracted from the China Statistical Yearbook 2022 , from which the total population and registered nurse numbers were collected from 2008 to 2021, and the registered nurses per thousand population were calculated.

Methods:

Based on data from the China Statistical Yearbook 2022, the grey GM (1,1) model was used to predict the demand for nursing jobs and geriatric nurses over the next 10 years (2024–2033).

Results:

The results indicate that from 2024 to 2033, amidst a continuous growth in the overall population and an increasingly pronounced trend of population aging, the demand for nursing workforce in China, especially for specialized geriatric nurses, is projected to steadily increase.

Conclusions:

The paper provides a reference basis for the establishment of China’s healthcare workforce system and the involvement of government departments in healthcare workforce planning.