Interpreting COVID-19 and Virtual Care Trends: A Call for Action.

Clicks: 270
ID: 103129
2020
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Background: The Coronavirus Disease (COVID-19) pandemic is rapidly spreading across the world. As of March 26th, 2020, there are more than 500,000 cases and more than 25,000 deaths related to COVID-19, and the number are increasing by the hour. Objective: The objective of this study was to study the trends in confirmed COVID-19 cases in North Carolina, along with understanding patterns of received virtual visits related to symptoms of COVID-19. Methods: We conducted a cohort study of patients using an on-demand, state-wide Virtual Urgent Care (VUC) center. We collected data from February 1, 2020 until March 15, 2020. Institutional Review Board exemption was obtained prior to the study. Results: Of the 733 total virtual visits, 257 (35%) were COVID-19 like symptoms. Of the COVID-19 like visits, the number of females was 178 (70%). People in the 30-39 years of age (26%) and 40-49 years (25%) were 50% of the total patients. Additionally, approximately 97% of the COVID-like encounters came from within the State of North Carolina. Our study shows that VC can provide efficient triaging in the counties were the highest number of COVID-19 cases. We also confirmed that the widespread of the disease occurs in areas of high population density as well as in areas with major airports. Conclusions: The use of Virtual Care presents promising potential in the fight against COVID-19. Virtual Care is capable to reduce ER visits, conserve healthcare resources, and avoid the spread of COVID-19 by treating patient remotely. We call on more adoption of Virtual Care by health systems across the U.S. and the world during the COVID-19 pandemic.
Reference Key
khairat2020interpretingjmir Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Khairat, Saif;Meng, Chenlu;Xu, Yuxuan;Edson, Barbara;Gianforcaro, Robert;
Journal jmir public health and surveillance
Year 2020
DOI
10.2196/18811
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.