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A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia.

作者信息

Prabhune Akash, Bhat Sachin, Mallavaram Aishwarya, Mehar Shagufta Ayesha, Srinivasan Surya

机构信息

Health and Information Technology, Institute of Health Management Research, Bangalore, IND.

Health, Public Affairs Centre, Bangalore, IND.

出版信息

Cureus. 2023 Nov 17;15(11):e48977. doi: 10.7759/cureus.48977. eCollection 2023 Nov.


DOI:10.7759/cureus.48977
PMID:38111408
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10726017/
Abstract

The objective of this paper was to evaluate and compare the quantity and sustainability of digital health initiatives in the South Asia region before and during the COVID-19 pandemic. The study used a two-step methodology of (a) descriptive analysis of digital health research articles published from 2016 to 2021 from South Asia in terms of stratification of research articles based on diseases and conditions they were developed, geography, and tasks wherein the initiative was applied and (b) a simple and replicable tool developed by authors to assess the sustainability of digital health initiatives using experimental or observational study designs. The results of the descriptive analysis highlight the following: (a) there was a 40% increase in the number of studies reported in 2020 when compared to 2019; (b) the three most common areas wherein substantive digital health research has been focused are health systems strengthening, ophthalmic disorders, and COVID-19; and (c) remote consultation, health information delivery, and clinical decision support systems are the top three commonly developed tools. We developed and estimated the inter-rater operability of the sustainability assessment tool ascertained with a Kappa value of 0.806 (±0.088). We conclude that the COVID-19 pandemic has had a positive impact on digital health research with an improvement in the number of digital health initiatives and an improvement in the sustainability score of studies published during the COVID-19 pandemic.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/6b88c8c8849b/cureus-0015-00000048977-i12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/f48f7e9e42f2/cureus-0015-00000048977-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/e1b0715dc408/cureus-0015-00000048977-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/abeb66cec65d/cureus-0015-00000048977-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/65a070ae7e99/cureus-0015-00000048977-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/0563d62f5e47/cureus-0015-00000048977-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/78fcc1fba876/cureus-0015-00000048977-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/5604d6766d2b/cureus-0015-00000048977-i07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/c219bafc2b9e/cureus-0015-00000048977-i08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/55afd9f1650a/cureus-0015-00000048977-i09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/c842ef29981a/cureus-0015-00000048977-i10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/0482f1a815a2/cureus-0015-00000048977-i11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/6b88c8c8849b/cureus-0015-00000048977-i12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/f48f7e9e42f2/cureus-0015-00000048977-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/e1b0715dc408/cureus-0015-00000048977-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/abeb66cec65d/cureus-0015-00000048977-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/65a070ae7e99/cureus-0015-00000048977-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/0563d62f5e47/cureus-0015-00000048977-i05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/78fcc1fba876/cureus-0015-00000048977-i06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/5604d6766d2b/cureus-0015-00000048977-i07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/c219bafc2b9e/cureus-0015-00000048977-i08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/55afd9f1650a/cureus-0015-00000048977-i09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/c842ef29981a/cureus-0015-00000048977-i10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/0482f1a815a2/cureus-0015-00000048977-i11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a29/10726017/6b88c8c8849b/cureus-0015-00000048977-i12.jpg

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A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia.

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引用本文的文献

[1]
The potential of digital health interventions to address health system challenges in Southeast Asia: A scoping review.

Digit Health. 2025-1-10

本文引用的文献

[1]
Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study.

Lancet Digit Health. 2021-1

[2]
Impact of teleophthalmology during COVID-19 lockdown in a tertiary care center in South India.

Indian J Ophthalmol. 2021-3

[3]
A comprehensive mobile application tool for disease surveillance, workforce management and supply chain management for Malaria Elimination Demonstration Project.

Malar J. 2021-2-16

[4]
Phone calls for improving blood pressure control among hypertensive patients attending private medical practitioners in India: Findings from Mumbai hypertension project.

J Clin Hypertens (Greenwich). 2021-4

[5]
Predicting Public Uptake of Digital Contact Tracing During the COVID-19 Pandemic: Results From a Nationwide Survey in Singapore.

J Med Internet Res. 2021-2-3

[6]
A pilot randomized controlled trial (RCT) of daily versus weekly interactive voice response calls to support adherence among antiretroviral treatment patients in India.

Mhealth. 2020-10-5

[7]
Impact of mHealth interventions for reproductive, maternal, newborn and child health and nutrition at scale: BBC Media Action and the program in Bihar, India.

J Glob Health. 2020-12

[8]
Validation of visual acuity applications for teleophthalmology during COVID-19.

Indian J Ophthalmol. 2021-2

[9]
A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

Lancet Digit Health. 2020-6

[10]
Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms.

Lancet Digit Health. 2020-10

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