A Study on Sustainable Business Growth of Private Telemedicine Businesses in India
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Abstract
This project aims to use the Health Belief Model (HBM) as a foundation to identify the crucial variables that influence the adoption of paid telemedicine services by individuals who reside in India and have access to mobile health. 355 individuals participated in a survey with 30 questions as part of the research to collect information.
The statistical analysis of the gathered data was done using exploratory factor analysis. The study revealed that individuals who felt more positively about using technology (ATT) had higher behavioral intent to use paid telemedicine. Individuals who valued using paid telemedicine more had higher perceived benefits (PBs). The findings also showed no significant association between increased Perceived Disease Threats (PDT), the severity and susceptibility of a condition, and an individual’s willingness to use telemedicine. Individuals with higher PBTAs (perceived barriers to action) demonstrated less enthusiasm for paid telemedicine use; higher PBTAs (perceived barriers to action) demonstrated less enthusiasm for using paid telemedicine. Individuals with more positive attitudes towards telemedicine also showed more cues to internal and external action
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References
Anand A, Trivedi NK, Gautam V, Arvindhan M. Infrastructure and Systems of Telemedicine. In: Telemedicine: The Computer Transformation of Healthcare. Cham: Springer International Publishing; 2022:29–41. Available from: http://dx.doi.org/10.1007/978-3-030-99457-0_3
Child D. The Essentials of Factor Analysis. 2nd ed. London: Cassel Educational Ltd; 1990. Available from: https://www.scirp.org/reference/referencespapers?referenceid=1102415
Meghani A, Hariyani S, Das P, Bennett S. Public sector engagement of private healthcare providers during the COVID-19 pandemic in Uttar Pradesh, India. PLOS Glob Public Health. 2022;2(7):e0000750. Available from: https://doi.org/10.1371/journal.pgph.0000750
Davis FD. A technology acceptance model for empirically testing new end-user information systems. Cambridge, MA; 1986:17.
Ganapathy K, Reddy S. Technology enabled remote healthcare in public-private partnership mode: A story from India. In: Telemedicine, Telehealth, and Telepresence: Principles, Strategies, Applications, and New Directions. Springer; 2021:197–233. Available from: https://link.springer.com/chapter/10.1007/978-3-030-56917-4_14
Khemapech I, Sansrimahachai W, Toachoodee M. Telemedicine–meaning, challenges, and opportunities. Siriraj Med J. 2019;71(3):246–252. Available from: https://doi.org/10.33192/Smj.2019.38
Jagarapu J, Savani RC. A brief history of telemedicine and the evolution of teleneonatology. Semin Perinatol. 2021;45(5):151416. Available from: https://doi.org/10.1016/j.semperi.2021.151416
Ganapathy KN. Apollo Hospitals, Chennai, Telemedicine in India—the Apollo experience. Neurosurgery on the Web. 2001.
Dasgupta A, Deb S. Telemedicine: A new horizon in public health in India. Indian J Community Med. 2008;33(1):3–8. Available from: https://doi.org/10.4103/0970-0218.39234
Gagandeep K, Rishabh M, Vyas S. Artificial Intelligence (AI) Startups in the Health Sector in India: Challenges and Regulation in India. In: Proceedings of the Third International Conference on Information Management and Machine Intelligence: ICIMMI 2021. Singapore: Springer; 2022:203–215. Available from: http://dx.doi.org/10.1007/978-981-19-2065-3_24
Huang JC, Lee YC. Model construction for the intention to use telecare in patients with chronic diseases. Int J Telemed Appl. 2013;2013:650238. Available from: https://doi.org/10.1155/2013/650238
Huang JC, Lin SP. Exploring the critical factors in the choice of home telehealth by using the health belief model. J Telemed Telecare. 2009;15(1):87–92. Available from: https://doi.org/10.1089/tmj.2008.0069