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| BRIEF RESEARCH ARTICLE |
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| Year : 2020 | Volume
: 64
| Issue : 6 | Page : 234-236 |
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Factors affecting the adoption of telemedicine during COVID-19
Vinaytosh Mishra
Assistant Professor, QT & OM Area, FORE School of Management, New Delhi, India
| Date of Submission | 22-Apr-2020 |
| Date of Decision | 04-May-2020 |
| Date of Acceptance | 11-May-2020 |
| Date of Web Publication | 2-Jun-2020 |
Correspondence Address:
Vinaytosh Mishra
FORE School of Management, B.18 Qutub Institutional Area, New Delhi
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijph.IJPH_480_20
 Abstract | | |
Novel coronavirus disease (COVID-19) has put restriction of travel, and social distancing has become a new normal. This outbreak of the pandemic has made telemedicine more relevant than ever. The objective of this study is to identify the factors affecting the rate of adoption of telemedicine and effect of the COVID-19 on these factors. The research develops five hypotheses to test the influence of a disease outbreak on the rate of telemedicine adoption. The method used for the study is the Wilcoxon signed-rank test, and the sampling method used for the study is purposive sampling. The respondents were taken from a multispecialty clinic in North India and the sample size for the study is 43. The study concludes that patients are seeing more value in the use of telemedicine during COVID-19. They are more willing to experiment with telemedicine and are not intimidated by the technology related to telemedicine.
Keywords: COVID-19, diffusion innovation theory, post-COVID world, technology adoption, telemedicine
How to cite this article:
Mishra V. Factors affecting the adoption of telemedicine during COVID-19. Indian J Public Health 2020;64, Suppl S2:234-6 |
| Introduction | |  |
Novel coronavirus disease (COVID-19) has become a major health challenge worldwide. The current crisis has made telemedicine as a preferred approach of delivery of health care.[1] It is not possible to create an effective telemedicine program overnight, but the health systems that have already implemented it can leverage it for the response to COVID-19.[2] The post-COVID world will witness increased spending by health-care systems on telemedicine.
Telemedicine refers to the use of telecommunication and information technologies to provide clinical health care to distant or isolated individuals. Using such technology, clinicians can examine patients and make treatment recommendations across long distances.[3] The technological advances including high-resolution video cameras and stable broadband Internet have helped make real-time telemedicine an increasingly common mode of health-care delivery.[4] Mehrotra et al., in their article, reported that telemedicine can increase access and improve the quality of care in remote areas.[5] Another study concludes that telemedicine may turn out to be the cheapest, as well as the fastest, way to bridge the rural–urban health divide in developing countries like India.[6] India has success stories of implementation of telemedicine, but the need for the hour is implementation at a large scale.[7]
Das and Pappuru in their research concluded that telemedicine monitoring should be designed for people who are in need and receptive to telemedicine.[8] They also concluded that receptive patients focus on convenience, whereas unreceptive patients strongly value their patient–physician relationships. The diffusion of innovation theory provides an appropriate framework to understand the adoption of telemedicine.[9] The theory defines five factors that influence the rate of adoption of innovation listed in [Table 1].
This study analyzes the effects of COVID-19 outbreak on the factors affecting the rate of adoption of telemedicine.
| Materials and Methods | | |
This research tests five hypotheses, one each for factors affecting the rate of adoption. The respondents for this study were selected from a multispecialty clinic in North India. The respondents were aged between 35 and 65 years and have used telemedicine before the outbreak of COVID-19. The respondents were asked to rate the factors affecting the rate of adoption on the Likert scale of 1 to 5, where 1 is the least while 5 is the highest. The method used for the study is the Wilcoxon signed-rank test. The sampling method used for this study is purposive sampling, while the sample size used in the study is 43.
| Results | | |
To test the effect of the COVID-19 on the rate of adoption of telemedicine, five hypotheses were formulated. The statistical test used for the testing hypothesis was the Wilcoxon signed-rank test. The results of the test are summarized in [Table 2].
Hypothesis 1
The first hypothesis to be tested is whether patients perceive the relative advantage (RAD) of the utility of telemedicine more during the COVID-19 outbreak.
- Ho= The mean rank of RAD is not higher during COVID-19
- HA= The mean rank of RAD is higher during COVID-19.
Because the P value is less than α/2, the null hypothesis gets rejected. Hence, we conclude that patients perceive the use of telemedicine as beneficial, during the COVID-19 outbreak.
Hypothesis 2
The second hypothesis to be tested is whether patients perceive the compatibility (COM) of telemedicine to be more advantageous and fit for use during the COVID-19 outbreak.
- Ho= The mean rank of COM is not higher during COVID-19
- HA= The mean rank of COM is higher during COVID-19.
Because the P value is less than α/2, the null hypothesis gets rejected. Hence, we conclude that patients perceive telemedicine to be more fit for use during the COVID-19 outbreak.
Hypothesis 3
The third hypothesis to be tested is whether patients perceive the trialability (TRI) of telemedicine more during the COVID-19 outbreak.
- Ho= The mean rank of TRI is not higher during COVID-19
- HA= The mean rank of TRI is higher during COVID-19.
Because the P value is less than α/2, the null hypothesis gets rejected. Hence, we conclude that patients are more open to trying the telemedicine during COVID-19 outbreak.
Hypothesis 4
The fourth hypothesis to be tested is whether patients experience and observe the added benefits more from telemedicine during the COVID-19 outbreak.
- Ho= The mean rank of observability (OBS) is not higher during COVID-19
- HA= The mean rank of OBS is higher during COVID-19.
Because the P value is again less than α/2, the null hypothesis gets rejected. Hence, we conclude that patients have observed more benefits of telemedicine during the COVID-19 outbreak.
Hypothesis 5
The last hypothesis to be tested is whether patients perceive telemedicine less complex to use during the COVID-19 outbreak.
- Ho= The mean rank of complexity (CLX) is not less during COVID-19
- HA= The mean rank of CLX is less during COVID-19.
Because the P value is less than α/2, the null hypothesis gets rejected. Hence, we conclude that patients found telemedicine less complex to use during the COVID-19 outbreak.
| Conclusion | | |
In a post-COVID-19 world, we will witness major changes in the way health-care services are delivered. The study concludes that during the COVID-19 outbreak, a patient perceives telemedicine to be useful and better suited for the delivery of health-care services than ever before. The patient is more willing to try telemedicine in the present scenario, which will result in increased adoption of telemedicine in the post-COVID world. The patients found extrinsic motivation such as reduced cost and reduced need for travel for making use of telemedicine facilities. Last but not the least, the use of some form of telemedicine has become essential for most people. They are not getting intimidated by technology and making efforts to embrace it. The findings of this study conclude that respondents perceived less complexity in the use of telemedicine post-COVID-19 outbreak.
Acknowledgment
The infrastructural support provided by FORE School of Management, New Delhi, is greatly appreciated.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
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| 2. | Hollander JE, Carr BG. Virtually perfect? Telemedicine for COVID-19. New England Journal of Medicine 2020;382:1679-81. |
| 3. | Kahn JM. Virtual visits-confronting the challenges of telemedicine. N Engl J Med 2015;372:1684-5. |
| 4. | Burke BL, Hall RW. Telemedicine: Pediatric applications. Pediatrics 2015;136:e293-308. |
| 5. | Mehrotra A, Jena AB, Busch AB, Souza J, Uscher-Pines L, Landon BE. Utilization of telemedicine among rural Medicare beneficiaries. Jama 2016;315:2015-6. |
| 6. | Dasgupta A, Deb S. Telemedicine: A new horizon in public health in India. Indian J Community Med 2008;33:3-8. [ PUBMED] [Full text] |
| 7. | Ganapathy K, Ravindra A. Telemedicine in India: The Apollo story. Telemed J E Health 2009;15:576-85. |
| 8. | Das T, Pappuru RR. Telemedicine in diabetic retinopathy: Access to rural India. Indian J Ophthalmol 2016;64:84. [ PUBMED] [Full text] |
| 9. | Rogers E. Diffusion of Innovations. 4 th ed. New York: The Free Press; 1995. |
[Table 1], [Table 2]
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