ABSTRACT. Employing recent research results covering digital epidemiological surveillance, smart telemedicine diagnosis systems, and machine learning-based real-time data sensing and processing in COVID-19 remote patient monitoring, and building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. The precision rate as regards diagnosis can be optimized through deep learning algorithms and smart networked medical devices. Deep neural network-driven Internet of Things and wearable devices are pivotal in patient-oriented medical real-time analytics and smart healthcare. Artificial intelligence-powered diagnostic tools and machine learning-based real-time data sensing and processing have been integrated into big healthcare data analytics. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: COVID-19; remote patient monitoring; telemedicine diagnosis; big data

How to cite: Woods, M., and Miklencicova, R. (2021). “Digital Epidemiological Surveillance, Smart Telemedicine Diagnosis Systems, and Machine Learning-based Real-Time Data Sensing and Processing in COVID-19 Remote Patient Monitoring,” American Journal of Medical Research 8(2): 65–77. doi: 10.22381/ajmr8220215.

Received 19 May 2021 • Received in revised form 12 October 2021
Accepted 15 October 2021 • Available online 28 October 2021