Design of a Telemedicine-based system for Clinic-In-A-Can

Sworo, George D.; Kam, Moshe; Juan, Estella J.

doi:10.1109/ghtc.2012.43

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Communication between nurses and doctors, nurses and community health workers (CHWs), and doctors and patients will be made easier by technology. [111] employs a system that combines long-term care, constant monitoring of a number of critical indicators, and an emergency cellular link to a hospital.…”

Section: Mobile Phone Base Telemedicine Network Designmentioning

confidence: 99%

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

AfricArXiv

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Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software-Defined Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. The first section of the article goes over the history of traffic and capacity expansion, as well as future projections. By introducing a medical and image data communication protocol for telemedicine, the second section examines the technological constraints of expanding capacity in the era of UAV & software defined networking. The third section discusses ways to maximize network capacity by considering quality of service (QoS) capacity issues. Finally, the article explores how to construct a telemedicine network that can provide performance, customization, and capabilities to keep up with increased traffic in the coming decades. Research gaps and future directions were presented in the last section.

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Section: Mobile Phone Base Telemedicine Network Designmentioning

confidence: 99%

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

AfricArXiv

View full text Add to dashboard Cite

show abstract

Section: Mobile Phone Base Telemedicine Network Designmentioning

confidence: 99%

Maximizing network capacity, control and management in designing a telemedicine network: A review and recent challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

Nig. J. Tech.

View full text Add to dashboard Cite

Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software-Defined Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. The first section of the article goes over the history of traffic and capacity expansion, as well as future projections. By introducing a medical and image data communication protocol for telemedicine, the second section examines the technological constraints of expanding capacity in the era of UAV & softwaredefined networking. The third section discusses ways to maximize network capacity by considering quality of service (QoS) capacity issues. Finally, the article explores how to construct a telemedicine network that can provide performance, customization, and capabilities to keep up with increased traffic in the coming decades. Research gaps and future directions were presented in the last section.

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“…Several studies aimed at improving service quality in e-referral systems have been conducted in [28,34,35]. Their researches developed e-referral system models by replacing the use of telephone, fax, and reference request in hardcopy format into electronic one.…”

Section: Related Work 21 Referral System Modelmentioning

confidence: 99%

E-Referral System Modeling Using Fuzzy Multiple-Criteria Decision Making

Triyono

Hartati

Pulungan

et al. 2018

IJEECS

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Currently, the quality of health services in Indonesia is getting better. However, the referral system still has some problems. The first is the difficulty in determining the referral hospital by the condition of the patient. The second is the different parameters used to determine the referral hospitals between countries. Based on those problems, it is necessary to improve the ability of the current referral system. This study developed a reference system model for assessing the suitability of the patient's background with the referral hospital. Some of the methods used are restful on web service technologies for information exchange between primary health care doctors and referral hospitals, Fuzzy Multiple-Criteria Decision Making (FMCDM) to determine the ranking of referral hospitals that fit the patient's background. The result of this study is an intelligent system model to get the referral hospital that fit the patient's background.

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Design of a Telemedicine-based system for Clinic-In-A-Can

Cited by 6 publications

References 2 publications

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Maximizing network capacity, control and management in designing a telemedicine network: A review and recent challenges

E-Referral System Modeling Using Fuzzy Multiple-Criteria Decision Making

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