ABSTRACT. The purpose of this study was to empirically examine medical big data and wearable Internet of Things healthcare systems in remotely monitoring and caring for confirmed or suspected COVID-19 patients. Building our argument by drawing on data collected from Accenture, Amwell, Deloitte, Ericsson ConsumerLab, Kyruus, The Rockefeller Foundation, Syneos Health, and USAID, we performed analyses and made estimates regarding artificial intelligence-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. Artificial intelligence-powered diagnostic tools have been instrumental in COVID-19 prevention, screening, and treatment. Internet of Medical Things enables medical data sharing essential in disease diagnosis and treatment. Deep machine learning and cloud computing are pivotal in Internet of Things-based healthcare. Internet of Medical Things systems enable remote patient monitoring as regards chronic diseases. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: medical big data; COVID-19; Internet of Things healthcare system

How to cite: Hurley, D., and Popescu, G. H. (2021). “Medical Big Data and Wearable Internet of Things Healthcare Systems in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients,” American Journal of Medical Research 8(2): 78–90. doi: 10.22381/ajmr8220216.

Received 7 May 2021 • Received in revised form 14 October 2021
Accepted 17 October 2021 • Available online 28 October 2021

Deborah Hurley
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Biomedical Big Data,
Belfast at AAER, Northern Ireland
(corresponding author)
Gheorghe H. Popescu
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The Center for Applied Macroeconomic Analysis
at AAER, New York City, NY, USA;
Dimitrie Cantemir Christian University, Bucharest, Romania

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