ReviewSN computer science2021
Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic.
Review in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 48 citations in OpenAlex.
- Impact of telehealth modalities on medication adherence in older adults with heart disease: Evidence from NHATS.Internet interventions · 2026Article
- COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach.JMIRx med · 2025Article
- Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation.BMC medical informatics and decision making · 2023Article
- Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.Multimedia tools and applications · 2023Article
- Survival study on deep learning techniques for IoT enabled smart healthcare system.Health and technology · 2023Article
- Telehealth reform post-public health emergency: crucial next steps.Proceedings (Baylor University. Medical Center) · 2023Article
- Wearable technology for early detection of COVID-19: A systematic scoping review.Preventive medicine · 2022Article
- Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images.BMC medical imaging · 2022Article
- Predictive modeling for COVID-19 readmission risk using machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Prognosis patients with COVID-19 using deep learning.BMC medical informatics and decision making · 2022Article
- A deep learning-driven low-power, accurate, and portable platform for rapid detection of COVID-19 using reverse-transcription loop-mediated isothermal amplification.Scientific reports · 2022Article
- Adoption of telehealth technologies: an approach to improving healthcare system.Translational medicine communications · 2022Review
- Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey.Frontiers in artificial intelligence · 2022Review
- Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.Scientific reports · 2021Article
- UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.BMC medical imaging · 2021Article
- ANFIS-Net for automatic detection of COVID-19.Scientific reports · 2021Article
- iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients.NPJ digital medicine · 2021Article
- Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.Scientific reports · 2021Article
- Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients.BMC medical informatics and decision making · 2021Article
- Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An ongoing pandemic, the novel coronavirus disease 2019 (COVID-19) is threatening the nations of the world regardless of health infrastructure conditions. In the age of digital electronic information and telecommunication technology, scalable telehealth services are gaining immense importance by helping to maintain social distances while providing necessary healthcare services. This paper aims to review the various types of scalable telehealth services used to support patients infected by COVID-19 and other diseases during this pandemic. Recently published research papers collected from various sources such as Google Scholar, ResearchGate, PubMed, Scopus, and IEEE Xplore databases using the terms "Telehealth", "Coronavirus", "Scalable" and "COVID-19" are reviewed. The input data and relevant reports for the analysis and assessment of the various aspects of telehealth technology in the COVID-19 pandemic are taken from official websites. We described the available telehealth systems based on their communication media such as mobile networks, social media, and software based models throughout the review. A comparative analysis among the reviewed systems along with necessary challenges and possible future directions are also drawn for the proper selection of affordable technologies. The usage of scalable telehealth systems improves the quality of the healthcare system and also reduces the infection rate while keeping both patients and doctors safe during the pandemic.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.