Telehealth for COVID-19: A Conceptual Framework.
Waqas YousafArif Iqbal UmarSyed Hamad ShiraziMuhammad FayazMuhammad AssamJaved Ali KhanAssad RasheedGulzar MehmoodPublished in: Journal of healthcare engineering (2023)
The world has been going through the global crisis of the coronavirus (COVID-19). It is a challenging situation for every country to tackle its healthcare system. COVID-19 spreads through physical contact with COVID-positive patients and causes potential damage to the country's health and economy system. Therefore, to overcome the chance of spreading the disease, the only preventive measure is to maintain social distancing. In this vulnerable situation, virtual resources have been utilized in order to maintain social distance, i.e., the telehealth system has been proposed and developed to access healthcare services remotely and manage people's health conditions. The telehealth system could become a regular part of our healthcare system, and during any calamity or natural disaster, it could be used as an emergency response to deal with the catastrophe. For this purpose, we proposed a conceptual telehealth framework in response to COVID-19. We focused on identifying critical issues concerning the use of telehealth in healthcare setups. Furthermore, the factors influencing the implementation of the telehealth system have been explored in detail. The proposed telehealth system utilizes artificial intelligence and data science to regulate and maintain the system efficiently. Before implementing the telehealth system, it is required that prearrangements be made, such as appropriate funding measures, the skills to know technological usage, training sessions, and staff endorsement. The barriers and influencing factors provided in this article can be helpful for future developments in telehealth systems and for making fruitful progress in fighting pandemics like COVID-19. At the same time, the same approach can be used to save the lives of many frontline workers.
Keyphrases
- healthcare
- sars cov
- coronavirus disease
- public health
- artificial intelligence
- mental health
- respiratory syndrome coronavirus
- machine learning
- end stage renal disease
- big data
- chronic kidney disease
- health information
- deep learning
- emergency department
- oxidative stress
- electronic health record
- prognostic factors
- patient reported outcomes
- human health
- data analysis