ABSTRACT. The aim of this paper is to synthesize and analyze existing evidence on virtual care technologies, wearable health monitoring sensors, and Internet of Medical Things-based smart disease surveillance systems in the diagnosis and treatment of COVID-19 patients. Using and replicating data from Deloitte, Ericsson ConsumerLab, GlobalWebIndex, McKinsey, PwC, Sony, and Sykes, we performed analyses and made estimates regarding artificial intelligence-based diagnostic algorithms in Internet of Things-supported healthcare delivery. Artificial intelligence-enabled wearable medical devices, virtualized care systems, and wireless biomedical sensing devices are pivotal in COVID-19 screening, testing, and treatment. Digital epidemiological surveillance in monitoring, detection, and prevention of COVID-19 is optimized by use of medical artificial intelligence, clinical and diagnostic decision support systems, machine learning-based real-time data sensing and processing, and smart healthcare devices and applications. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: virtual care; Internet of Medical Things; COVID-19; sensing device

How to cite: Maxwell, S., and Grupac, M. (2021). “Virtual Care Technologies, Wearable Health Monitoring Sensors, and Internet of Medical Things-based Smart Disease Surveillance Systems in the Diagnosis and Treatment of COVID-19 Patients,” American Journal of Medical Research 8(2): 118–131. doi: 10.22381/ajmr8220219.

Received 17 May 2021 • Received in revised form 21 October 2021
Accepted 24 October 2021 • Available online 28 October 2021