An online COVID-19 self-assessment framework supported by IoMT technology

Mohammed Kamal Nsaif*, Bilal Adil Mahdi, Yusor Rafid Bahar Al-Mayouf, Omar Adil Mahdi, Ahmed J. Aljaaf, Suleman Khan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Abstract As COVID-19 pandemic continued to propagate, millions of lives are currently at risk especially elderly, people with chronic conditions and pregnant women. Iraq is one of the countries affected by the COVID-19 pandemic. Currently, in Iraq, there is a need for a self-assessment tool to be available in hand for people with COVID-19 concerns. Such a tool would guide people, after an automated assessment, to the right decision such as seeking medical advice, self-isolate, or testing for COVID-19. This study proposes an online COVID-19 self-assessment tool supported by the internet of medical things (IoMT) technology as a means to fight this pandemic and mitigate the burden on our nation’s healthcare system. Advances in IoMT technology allow us to connect all medical tools, medical databases, and devices via the internet in one collaborative network, which conveys real-time data integration and analysis. Our IoMT framework-driven COVID-19 self-assessment tool will capture signs and symptoms through multiple probing questions, storing the data to our COVID-19 patient database, then analyze the data to determine whether a person needs to be tested for COVID-19 or other actions may require to be taken. Further to this, collected data can be integrated and analyzed collaboratively for developing a national health policy and help to manage healthcare resources more efficiently. The IoMT framework-driven online COVID-19 self-assessment tool has a big potential to prevent our healthcare system from being overwhelmed using real-time data collection, COVID-19 databases, analysis, and management of people with COVID-19 concerns, plus providing proper guidance and course of action.
Original languageEnglish
Pages (from-to)966-975
Number of pages10
JournalJournal of Intelligent Systems
Volume30
Issue number1
Early online date4 Jul 2021
DOIs
Publication statusPublished - 6 Aug 2021

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