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Applying software-defined networking to support telemedicine health consultation during and post Covid-19 era.
Jnr, Bokolo Anthony; Nweke, Livinus Obiora; Al-Sharafi, Mohammed A.
  • Jnr BA; Department of Computer Science, Norwegian University of Science and Technology, NTNU, NO-7491 Trondheim, Norway.
  • Nweke LO; Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway.
  • Al-Sharafi MA; Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, 26300 Gambang, Malaysia.
Health Technol (Berl) ; 11(2): 395-403, 2021.
Article in English | MEDLINE | ID: covidwho-909047
ABSTRACT
The novel coronavirus disease-19 (COVID-19) infection has altered the society, economy, and entire healthcare system. Whilst this pandemic has presented the healthcare system with unprecedented challenges, it has rapidly promoted the adoption of telemedicine to deliver healthcare at a distance. Telemedicine is the use of Information and Communication Technology (ICT) for collecting, organizing, storing, retrieving, and exchanging medical information. But it is faced with the limitations of conventional IP-based protocols which makes it challenging to provide Quality of Service (QoS) for telemedicine due to issues arising from network congestion. Likewise, medical professionals adopting telemedicine are affected with low QoS during health consultations with outpatients due to increased internet usage. Therefore, this study proposes a Software-Defined Networking (SDN) based telemedicine architecture to provide QoS during telemedicine health consultations. This study utilizes secondary data from existing research works in the literature to provide a roadmap for the application of SDN to improve QoS in telemedicine during and after the COVID-19 pandemic. Findings from this study present a practical approach for applying SDN in telemedicine to provide appropriate bandwidth and facilitate real time transmission of medical data.
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Full text: Available Collection: International databases Database: MEDLINE Topics: Long Covid Language: English Journal: Health Technol (Berl) Year: 2021 Document Type: Article Affiliation country: S12553-020-00502-w

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Full text: Available Collection: International databases Database: MEDLINE Topics: Long Covid Language: English Journal: Health Technol (Berl) Year: 2021 Document Type: Article Affiliation country: S12553-020-00502-w