Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?


Currently submitted to: Journal of Medical Internet Research

Date Submitted: Sep 22, 2020
Open Peer Review Period: Sep 22, 2020 - Nov 17, 2020
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Emergency response to the COVID-19 pandemic using digital health technologies: practical experience of a tertiary hospital in China

  • Wanmin Lian; 
  • Li Wen; 
  • Qiru Zhou; 
  • Weijie Zhu; 
  • Wenzhou Duan; 
  • Xiongzhi Xiao; 
  • Mhungu Florence; 
  • Wenchen Huang; 
  • Chongchong Li; 
  • Weibin Cheng; 
  • Junzhang Tian



The outbreak of the novel corona virus disease (COVID-19) has caused a continuing global pandemic. Hospitals are integral in the control and prevention of COVID-19 but are met with numerous challenges in the midst of the epidemic.


The objective of our study was to introduce the practical experience of design and implementation, as well as the preliminary results, of an online COVID-19 service platform from a tertiary hospital in China.


The online COVID-19 service platform was deployed within the healthcare system of the Guangdong Second Provincial General Hospital-Internet Hospital, a program function which provides online medical services for both public individuals and lay-healthcare workers. The focal functions of this system include COVID-19 automated screening, related symptoms monitoring, online consultation, psychological support, and it also serves as a COVID-19 knowledge hub. The design and process of each functionality were introduced. The platform services usage data were collected and represented by three periods: the pre-epidemic period (2019.12.22~2020.1.22), the outbreak period (2020.1.23~2020.3.31), and the post-epidemic period (2020.4.1~2020.6.30).


By the end of June 2020, the COVID-19 automated screening and symptoms monitoring system had been used by 96,642 people for 161,884 and 7,795,194 person-times. The general online consultation service volume scaled up from 930 visits per-month in pre-epidemic period to over 8406 visits during the outbreak period, and dropped to 2218 visits in the post-epidemic period. The psychological counseling program served 636 clients during epidemic period. For people who used the COVID-19 automated screening service, overall, 160,916 (99%) of the users were classified under the no risk category. Less than 464 (0.3%) of the people were categorized under the medium to high risk class, and 12 people (0.01%) were recommended for COVID-19 treatment. Among the 96,642 individuals who used the COVID-19 related symptoms monitoring service, 6,696(6.9%) were symptomatic at some points during monitoring period. Fever was the most frequently reported symptom, with 40% of the people having had this symptom. Cough (25%) and sore throat (24%) were also relatively frequently reported among the symptomatic clients.


The online COVID-19 service platform exhibited as a role model for using digital health technologies to respond to the COVID-19 pandemic from a tertiary hospital in China. The digital solutions of COVID-19 automated screening, daily symptoms monitoring, online care service, and knowledge propagation have plausible acceptability and feasibility for complementing offline hospital services and facilitating disease control and prevention.


Please cite as:

Lian W, Wen L, Zhou Q, Zhu W, Duan W, Xiao X, Florence M, Huang W, Li C, Cheng W, Tian J

Emergency response to the COVID-19 pandemic using digital health technologies: practical experience of a tertiary hospital in China

JMIR Preprints. 22/09/2020:24505

DOI: 10.2196/preprints.24505


Download PDF

Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.