Affordable and scalable remote monitoring platforms for disease characterisation and patient management in pandemics and for high-consequence infections
- Funded by Wellcome Trust
- Total publications:4 publications
Grant number: 225437/Z/22/Z
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Key facts
Disease
COVID-19Start & end year
20222023Known Financial Commitments (USD)
$422,792.43Funder
Wellcome TrustPrincipal Investigator
Prof Catherine Louise ThwaitesResearch Location
United KingdomLead Research Institution
University of OxfordResearch Priority Alignment
N/A
Research Category
Clinical characterisation and management
Research Subcategory
Supportive care, processes of care and management
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Characterising new and emerging life-threatening infectious diseases safely, and optimising clinical management strategies, are essential early activities in an epidemic/pandemic response. New technologies, wearable devices, cloud computing, and machine learning (ML), offer potentially transformative solutions. Vietnam has just experienced its first major COVID-19 wave. Cases are rising again. In wave one, a shortage of monitoring equipment caused serious delays in identifying and treating those with severe disease. Conventional remote-monitoring systems, needed for safe monitoring, cost upwards of £12,000 for a single patient which is unfeasible in Vietnam. Our objective therefore is to provide proof-of principle that a team within Vietnam can establish a low-cost remote monitoring platform during a pandemic that will aid clinical care and capture data for disease characterisation and research. Our multidisciplinary team in Ho Chi Minh city will develop the platform from a prototype implemented in 50 patients during wave one. The new platform will provide real-time continuous remote monitoring of oxygen saturations and heart rate in 150 patients. We will integrate device, clinical, and laboratory data, and develop a prototype ML-based clinical decision support system for COVID-19. The platform is designed to be rapidly deployable in other low-resource settings and for other emerging infectious diseases.
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