Evidence map›Paper›PMID 32287040›Full record

ArticleJournal of medical Internet research2020

Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China.

Mengchun Gong, Li Liu, Xin Sun, Yue Yang, Shuang Wang, Hong Zhu

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  15. Virtual Infection Prevention and Control in Low- and Middle-Income Countries.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
    Review
  16. Persian mobile health applications for COVID-19: A use case-based study.Journal of education and health promotion · 2022
    Article
  17. Article
  18. Review
  19. Managing Pandemics with Health Informatics.Yearbook of medical informatics · 2021
    Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mengchun Gong *Institute of Health Management, Southern Medical University, Guangzhou, China.ORCID 0000-0001-8197-6643
Li Liu *Nanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0002-5188-2408
Xin SunChinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0002-6554-7088
Yue YangNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0002-9825-2614
Shuang WangInstitutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.ORCID 0000-0001-6420-983X
Hong ZhuInstitute of Health Management, Southern Medical University, Guangzhou, China.ORCID 0000-0002-9969-3542

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance.

objectiveThe aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China.

methodsThe HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices.

resultsHHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation.

conclusionsThe facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

Indexed as

Cloud ComputingDecision Support Systems, ClinicalSentinel SurveillanceBetacoronavirusChinaCoronavirusCoronavirus InfectionsCOVID-19Delivery of Health CareHumansMobile ApplicationsPandemicsPatient DischargePneumonia, ViralPublic HealthSARS-CoV-2clinical decision supportcloud systemCOVID-19medical informaticspandemicstakeholders involvementsyndromic surveillance

Identifiers

PMID32287040
PMCPMC7179239

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.