Unveiling Challenges and Opportunities: Examining COVID-19's Effects on Digital Health Research Initiatives in South Asia

                The aim of this study was to assess and contrast the volume and viability of digital health initiatives in the South Asia region both before the onset of the COVID-19 pandemic and during its course.

                To achieve this objective, a two-fold methodology was employed. Firstly, a descriptive analysis was conducted on digital health research articles published from 2016 to 2021 in South Asia [1,2]. This analysis involved stratifying research articles based on the diseases and conditions they addressed, the geographical areas of focus, and the tasks for which the initiatives were applied. Secondly, the study introduced a straightforward and replicable tool, developed by the authors, to evaluate the sustainability of digital health initiatives. This tool utilized experimental or observational study designs for assessment [4].

                The findings from the descriptive analysis revealed significant insights, including a 40% increase in the number of studies reported in 2020 compared to 2019. Noteworthy areas of focus included Health Systems Strengthening, Ophthalmic Disorders, and COVID-19. Additionally, the top three commonly developed tools were identified as Remote Consultation, Health Information Delivery, and Clinical Decision Support Systems [5,6].

                The inter-rater operability of the sustainability assessment tool was meticulously developed and estimated, resulting in a Kappa value of 0.806 (± 0.088).

                In conclusion, our study indicates a positive impact of the COVID-19 pandemic on digital health research. This is evidenced by an upswing in the number of digital health initiatives and an enhancement in the sustainability score of studies published during the COVID-19 period.

1. Fore, H. H. (2020). A wake-up call: COVID-19 and its impact on children’s health and wellbeing. Lancet Global Health, 8(7), 861-862, DOI: 10.1016/S2214-109X(20)30238-2.

2. Brown, R. B. (2020). Public health lessons learned from biases in coronavirus mortality overestimation. Disaster Medicine and Public Health Preparedness, 14(3), 364-371, DOI: 10.1017/dmp.2020.298.

3. Sustainability assessment methods and tools to support decision-making in the process industries | SAMT Project | H2020. CORDIS. European Commission [Website]. Retrieved April 6, 2021, from https://cordis.europa.eu/project/id/636727.

4. Taisch, M., Sadr, V., May, G., Stahl, B. (2013). Sustainability Assessment Tools – State of Research and Gap Analysis. In: Prabhu, V., Taisch, M., Kiritsis, D., editors. Advances in Production Management Systems Sustainable Production and Service Supply Chains. APMS 2013. Berlin, Heidelberg: Springer Berlin Heidelberg, 415, 426-434. Retrieved April 6, 2023, from http://link.springer.com/10.1007/978-3-642-41263-9_53.

5. Hallin, A., Karrbom-Gustavsson, T. (2010). Organizational Communication and Sustainable Development: ICTs for Mobility. Hershey, PA: IGI Global, 348 p.

6. Villeneuve, C., Tremblay, D., Riffon, O., Lanmafankpotin, G. Y., Bouchard, S. (2017). A Systemic Tool and Process for Sustainability Assessment. Sustainability, 9(10), 1909, DOI: 10.3390/su9101909.

7. PSAT. PSAT/CSAT [Website]. Retrieved May 16, 2023, from https://sustaintool.org/psat/.

8. CSAT. PSAT/CSAT [Website]. Retrieved May 16, 2023, from https://sustaintool.org/csat/.

9. Aspden, P., Corrigan, J. M., Wolcott, J., Erickson, S. M. (2004). Institute of Medicine (US) Committee on Data Standards for Patient Safety. Patient Safety: Achieving a New Standard for Care. – Washington (DC): National Academies Press (US), 4, Retrieved June 10, 2023, from https://www.ncbi.nlm.nih.gov/books/NBK216088/.

10. Hofman, W., Rajagopal, M. (2014). A Technical Framework for Data Sharing. Journal of Theoretical and Applied Electronic Commerce Research, 9(3), 45-58.

11. Deldar, K., Bahaadinbeigy, K., Tara, S. M. (2016). Teleconsultation and Clinical Decision Making: a Systematic Review. Acta Informatica Medica, 24(4), 286-292.

12. Balakrishnan, K., Honavar, V. (1998). Intelligent Diagnosis Systems. Journal of Intelligent Systems, 8(3-4), 239-290, DOI: 10.1515/JISYS.1998.8.3-4.239.

13. Demiris, G., Iribarren, S. J., Sward, K., Lee, S., Yang, R. (2019). Patient generated health data use in clinical practice: A systematic review. Nursing Outlook, 67(4), 311-330.

14. Predictive Analytics (2021). IBM [Website]. Retrieved April 8, 2023, from https://www.ibm.com/analytics/predictive-analytics.

15. Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., et al. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534.

16. Ristevski, B., Chen, M. (2018). Big Data Analytics in Medicine and Healthcare. Journal of Integrative Bioinformatics, 15(3), 1-5, DOI: 10.1515/jib-2017-0030.

17. Oleske, D. M. (2009). Screening and Surveillance for Promoting Population Health. Epidemiology and the Delivery of Health Care Services: Methods and Applications, 131-150, DOI: 10.1007/9781-4419-0164-4.

18. Li, Q. R. (2020). Overview of Data Visualization. Embodying Data. Springer Singapore, 17-47, DOI: 10.1007/978-981-15-5069-0_2.

19. WHO | Contact tracing. World Health Organization [Website]. Retrieved July 11, 2023, from https://www.who.int/csr/resources/publications/ebola/contact-tracing-guidelines/en/.

20. Swendeman, D., Fehrenbacher, A. E., Roy, S., Ray, P., Sumstine, S., Scheffler, A. et al. (2020). A pilot randomized controlled trial (RCT) of daily versus weekly interactive voice response calls to support adherence among antiretroviral treatment patients in India. Mhealth, 6, DOI: 10.21037/mhealth-19-248a

21. Shekhawat, N. S., Niziol, L. M., Sharma, S. S., Joseph, S., Robin, A. L., Gillespie, B. W. et al. (2020). The Utility of Routine Fundus Photography Screening for Posterior Segment Disease: A Stepped-wedge, Cluster-randomized Trial in South India. Ophthalmology, 128(7), 1060-1069.