A study on proactive health management initiatives in community setting

Abstract

The Healthy China 2030 Plan emphasizes proactive health management, advocating for individuals to actively maintain and promote their physical, mental, and social well-being. However, effectively implementing proactive health strategies in China and other aging populations remains challenging, particularly given the significant role of Social Determinants of Health (SDoH)—factors encompassing economic, social, and cultural conditions influencing individual health outcomes. Despite their critical importance, comprehensive research examining SDoH in the context of proactive health, specifically within Chinese communities, is limited. This study addresses this research gap by thoroughly investigating proactive health management initiatives within community settings, emphasizing the critical role of SDoH. We conducted a systematic literature review that provides robust evidence highlighting the considerable influence of SDoH on proactive health across multiple chronic and non-communicable diseases. Recognizing the challenges associated with extracting accurate SDoH data from Chinese electronic medical records (EMRs), we developed and validated an innovative AI-based information extraction and standardization technology tailored specifically to Chinese medical datasets. To validate this technology, we utilized a large-scale, multicenter dataset integrated through the National Key Research and Development Program of China. This dataset included records from over three million patients from 40 leading hospitals nationwide, such as the Affiliated Hospital of Southern Medical University and the Affiliated Children's Hospital of Fudan University. Our AI-based system demonstrated rapid, accurate, and efficient extraction and standardization of SDoH information, significantly clarifying their role in proactive health management. While the results strongly support the efficacy of our NLP-based technology, we also critically evaluated the potential limitations and biases inherent in the dataset to enhance the reliability of our findings. Our findings emphasize the necessity of integrating culturally and contextually relevant SDoH factors into proactive health strategies in Chinese community settings. The study offers actionable insights and recommendations tailored to healthcare providers, policymakers, and community health organizations, facilitating their ability to utilize big data platforms effectively. Specific implementation strategies and illustrative case studies are discussed to showcase the practical applicability of these recommendations, ultimately aiming to improve population health outcomes and support future clinical and public health research in China.