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COVID-19 and healthcare system in China: challenges and progression for a sustainable future
Open accessAbstract
With the ongoing COVID-19 outbreak, healthcare systems across the world have been pushed to the brink. The approach of traditional healthcare systems to disaster preparedness and prevention has demonstrated intrinsic problems, such as failure to detect early the spread of the virus, public hospitals being overwhelmed, a dire shortage of personal protective equipment, and exhaustion of healthcare workers. Consequently, this situation resulted in manpower and resource costs, leading to the widespread and exponential rise of infected cases at the early stage of the epidemic. To limit the spread of infection, the Chinese government adopted innovative, specialized, and advanced systems, including empowered Fangcang and Internet hospitals, as well as high technologies such as 5G, big data analysis, cloud computing, and artificial intelligence. The efficient use of these new forces helped China win its fight against the virus. As the rampant spread of the virus continues outside China, these new forces need to be integrated into the global healthcare system to combat the disease. Global healthcare system integrated with new forces is essential not only for COVID-19 but also for unknown infections in the future.
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“In March 2020, there were concerns among health system partners and public health agencies that a surge in COVID-19 patients might overburden the health system, as seen in China and New York City [50, 51].”
From Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care (2025) · cites this paper as [50]
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Metadata
- Authors
- Sun, S., Xie, Z., Yu, K., Jiang, B., Zheng, S., & Pan, X.
- Affiliations
- Wenzhou Medical University (CN)
- Corresponding author
- Pan, Xiaoting
- Venue
- Globalization and Health
- Published
- 2021-01-21
- Volume
- 17
- Issue
- 1
- Pages
- 14-14
- ISSN
- 1744-8603 (ISSN-L)
- PMC
- PMC7819629
- OpenAlex
- W3122906654
- Cited by
- 191
- References
- 22
- Citations by year
- 2026: 3 · 2025: 27 · 2024: 45 · 2023: 38 · 2022: 51 · 2021: 26
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Globalization and Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National College Students Innovation and Entrepreneurship Training Program (201810343007)
- Wenzhou Municipal Science and Technology Bureau (S20190024)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Systems and Practices
- Physical Sciences › Computer Science › Information Systems › Healthcare during COVID-19 Pandemic
- Health Sciences › Health Professions › Emergency Medical Services › Disaster Response and Management
- Keywords
- COVID-19 pandemic, health care system, disaster preparedness, internet hospitals, personal protective equipment shortage, 5G, big data analytics, cloud computing, artificial intelligence, China, emerging infectious diseases
- MeSH terms
- COVID-19, Artificial Intelligence, China, Disaster Planning, Disease Outbreaks, Government, Health Personnel, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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