Design of National Smart Public Health Emergency Management System
In order to solve the issues/problems existing in the current public health emergency management system in China, such as imperfect cross-sectoral collaborative management model, incomplete emergency response mechanism caused by long-term medical care and prevention fragmentation, difficulty in early screening, discovery, timely warning and continuous dynamic monitoring of major infectious diseases, ineffective information support, and unformed intelligent emergency management model, we integrated emerging information technologies such as big data (BD) and artificial intelligence (AI) into public health emergency management, designed a new intelligent public health emergency management model, and established an early identification and early risk warning model of public health emergencies. The knowledge base and model base of infectious disease prevention and control were built, the intelligent pre-hospital emergency treatment and emergency disposal system were studied, the intelligent public health emergency management platform was developed and pilot application was carried out in order to make up for the shortcomings of traditional emergency management work, realize intelligent public health risk identification, disease prevention and control and emergency handling, and improve public health emergency management service capacity.
Authors
- Panpan Qin (ORCID: https://orcid.org/0000-0002-8687-7721)
- Qingkun Chen (ORCID: https://orcid.org/0000-0002-8003-3149)
- Hongpu Hu (ORCID: https://orcid.org/0000-0003-2675-9384)
- Xing Gao (ORCID: https://orcid.org/0000-0001-9944-3626)
- Yanli Wan (ORCID: https://orcid.org/0000-0002-7307-2027)
- Jianhong Yao (ORCID: https://orcid.org/0000-0003-4204-2551)
- Qile He (ORCID: https://orcid.org/0000-0002-6218-9553)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1145/3708036.3708135
- Citations
- 1
- Primary Topic
- Innovation in Digital Healthcare Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.71