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. 2021 Feb 26;4(1):40.
doi: 10.1038/s41746-021-00412-9.

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

Dinesh Visva Gunasekeran #  1   2 , Rachel Marjorie Wei Wen Tseng #  1 , Yih-Chung Tham  1   3 , Tien Yin Wong  4   5   6
Affiliations

Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

Dinesh Visva Gunasekeran et al. NPJ Digit Med. .

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.

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Conflict of interest statement

D.G. reports equity investment in digital health solutions AskDr, Doctorbell (acquired by Mobile Health), VISRE, and Shyfts, and appointments as physician leader (telemedicine) in Raffles Medical Group and senior lecturer (medical innovation) at the National University of Singapore. T.W. holds patents of deep learning systems for detection of eye diseases. T.W. is the deputy group chief executive officer (research and education) of Singapore Health Services, a consultant & advisory board for Allergan, Bayer, Boehringer-Ingelheim, Genentech, Merck, Novartis, Oxurion (formerly ThromboGenics), Roche, and co-founder of plano and EyRiS. The remaining authors R.T. and Y.T. declare no competing financial interests.

Figures

Fig. 1
Fig. 1. Public health applications of digital health described in COVID-19.
Spider diagram of (a) clinical applications and (b) operational applications for the digital health technology domains described in COVID-19. Scale for the radial axes of this chart are standardized at 10 units per layer.
Fig. 2
Fig. 2. Bubble plot of translational relevance and strength of evidence for included reports.
The scales for “Translational relevance” and “Strength of evidence” are applied based on study design, participant recruitment and follow-up as described in the Methodology section.
See this image and copyright information in PMC

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