Prof. Tian Xinmin and Prof. Lu Lin from Antai College of Economics and Management (ACEM) undertook the Philosophy and Social Science Program of Shenzhen, and analyzed the establishment of grass-roots medical and health systems in megacities with Chinese characteristics. Due to the large population density and the large number of residents of China's megacities, the grass-roots medical and health agencies should act as the main body to respond to the residents' demand for health care services. However, most community-based health service centers are developed from hospitals at the street-block-level or sub-district level lacking due attention for a long time, so their medical workers always have poor professional knowledge and skills, and there is a high turnover of medical workers in community-based health service centers. Even with the country's ongoing increase in capital and human resources investment, it is still a great challenge for megacities to rapidly improve their grassroots medical services in the near future if traditional models and practices are retained. Therefore, the introduction of the family doctor system has been put on the agenda. This system, which draws on the successful experience of primary healthcare systems in developed European countries, will help optimize the operation of our grassroots medical facilities in megacities. However, it should also be noted that, in consideration of the difference in national conditions, population base and medical system, we should not copy them blindly when learning from them. Instead, we should structurally design and reform our medical and health resources based on our existing advantages and unique conditions to effectively avoid the difficulties encountered by developed countries in Europe and establish a grass-roots medical and health system with Chinese characteristics in megacities.

Digitalization and the application of artificial intelligence technologies offer a new solution for enhancing the service of grassroots public medical institutions in megacities and making up for the shortage of human resources in this field.

Specifically, efforts can be made from the following aspects:

I. Cross-sectoral establishment, maintenance and management of a unified mega-health data platform. The health administrative authorities should make uniform deployment to establish electronic health records for residents, break through the barriers among medical institutions at all levels and administrative regions, and promote the integration and synergy of individual health management information throughout the life cycle.

II. Digital and intelligent reshaping of the processes and nodes of grass-roots medical and health administration. For example, the medical workers of community-based health service centers used to take a lot of time to enter and manage residents' health data. We can apply various electronic devices and data systems to automatically record urban residents' data in the health monitoring system and perform normalized analysis and processing. This can minimize the repetitive work of grass-roots medical workers and improve the timeliness and accuracy of public health and medical information.

III. Expansion and enrichment of the administration scope of the grassroots medical and health system. The competent government authorities should invest human, financial and material resources in advance in a planned way, consolidate relevant administrative functions, and shift the resource allocation and assessment orientation on hardware rather than software, form rather than content, thereby promoting the efficient operation of the digital and intelligent grass-roots medical and health system.

IV. Cultivation and incubation of digital and intelligent technology innovation in grass-roots medical and health care system. Domestic AI technology has made some advances in medical detection and diagnosis at present, but it is still a long journey from mature and widespread commercial application. Governments of large cities should take the initiative to bear reasonable trial and error costs and guarantee the funds and grass-roots medical market of hopeful AI high-tech enterprises.