Literature DB >> 32288414

Demand Analysis and Management Suggestion: Sharing Epidemiological Data Among Medical Institutions in Megacities for Epidemic Prevention and Control.

Qinyi Cai1, Yiqun Mi1, Zhaowu Chu2, Yuanyi Zheng1, Fang Chen1, Yicheng Liu1.   

Abstract

During the prevention of coronavirus disease 2019 (COVID-19), epidemiological data is essential for controlling the source of infection, cutting off the route of transmission, and protecting vulnerable populations. Following Law of the People's Republic of China on Prevention and Treatment of Infectious Diseases and other related regulations, medical institutions have been authorized to collect the detailed information of patients, while it is still a formidable task in megacities because of the significant patient mobility and the existing information sharing barrier. As a smart city which strengthens precise epidemic prevention and control, Shanghai has established a multi-department platform named "one-net management" on dynamic information monitoring. By sharing epidemiological data with medical institutions under a safe environment, we believe that the ability to prevent and control epidemics among medical institutions will be effectively and comprehensively improved. © Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature 2020.

Entities:  

Keywords:  big data sharing; epidemic prevention and control; medical institutions; megacities

Year:  2020        PMID: 32288414      PMCID: PMC7137855          DOI: 10.1007/s12204-020-2166-3

Source DB:  PubMed          Journal:  J Shanghai Jiaotong Univ Sci


  7 in total

1.  A community-level study on COVID-19 transmission and policy interventions in Wuhan, China.

Authors:  Zhe Gao; Siqin Wang; Jiang Gu; Chaolin Gu; Regina Liu
Journal:  Cities       Date:  2022-05-13

Review 2.  Remote nursing training model combined with proceduralization in the intensive care unit dealing with patients with COVID-19.

Authors:  Hui Wang; Kai Kang; Yang Gao; Bo Yang; Jing Li; Lei Wang; Ying Bi; Kai-Jiang Yu; Qing-Qing Dai; Ming-Yan Zhao
Journal:  World J Clin Cases       Date:  2021-02-16       Impact factor: 1.337

3.  The challenge of privacy and security when using technology to track people in times of COVID-19 pandemic.

Authors:  Hermanus J Smidt; Osden Jokonya
Journal:  Procedia Comput Sci       Date:  2021-02-22

4.  Rehabilitation of Sepsis Patients with Acute Kidney Injury Based on Intelligent Medical Big Data.

Authors:  Yanmei Xia; Xiuzhe Wang; Weidong Wu; Haipeng Shi
Journal:  J Healthc Eng       Date:  2022-01-07       Impact factor: 2.682

5.  Evaluation of the Effect of PDCA in Hospital Health Management.

Authors:  Huanmin Qiu; Weiwei Du
Journal:  J Healthc Eng       Date:  2021-12-20       Impact factor: 2.682

6.  The application framework of big data technology during the COVID-19 pandemic in China.

Authors:  Wenyu Chen; Ming Yao; Liang Dong; Pingyang Shao; Ye Zhang; Binjie Fu
Journal:  Epidemiol Infect       Date:  2022-03-29       Impact factor: 2.451

7.  Diagnostic Value of SonoVue Contrast-Enhanced Ultrasonography in Nipple Discharge Based on Artificial Intelligence.

Authors:  Ling Guo
Journal:  J Healthc Eng       Date:  2021-12-16       Impact factor: 2.682

  7 in total

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