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ORIGINAL ARTICLE
Year : 2017  |  Volume : 3  |  Issue : 2  |  Page : 69-75

Using the Unified Theory of Acceptance and Use of Technology model to analyze cloud-based mHealth service for primary care


1 Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh; WHO Collaborating Centre on eHealth, The University of New South Wales, Kensington, NSW, Australia
2 Department of Banking and Insurance, Faculty of Business Studies, University of Dhaka, Dhaka, Bangladesh
3 WHO Collaborating Centre on eHealth, The University of New South Wales, Kensington, NSW, Australia; Centre For Entrepreneurship, University of Michigan-Shanghai Jiao Tong University Joint Institute, China

Correspondence Address:
Fatema Khatun
WHO Collaborating Centre on eHealth, The University of New South Wales Sydney, Kensington, NSW 2052, Australia; Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka 1212, Bangladesh

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/digm.digm_21_17

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Background and Objectives: Cloud-based mHealth services have the potential to make quality healthcare available in remote locations in the world. A practical deployment will involve medicolegal issues involving physicians and patients in different within and across countries. However, the first step is to evaluate such a cloud-based mHealth (MyOnlineClinic). This study aimed to understand and find out the factors that influence the end-user intention to use this new technology in Australia. Materials and Methods: We surveyed 167 end-users in 2015 and performed a Structural Equation Model analysis using Smart PLS to identify the intention to use the system among the participants. Results: The study revealed that the Unified Theory of Acceptance and Use of Technology construct, particularly facilitating condition (FC) (β = 0.355, P = 0.002), has yielded a significant influence on the behavioral intention to use MyOnlineClinic. However, the relationships between performance expectancy and behavioral intention (β = 0.162, P = 0.141), effort expectancy and behavioral intention (β = −0.004, P = 0.971), and social influences and behavioral intention (β = 0.164, P = 0.100) were insignificant. Further, age showed moderating effect on these variables. The majority of the respondents agreed or strongly agreed that technological issues such as sound (92.2%), video qualities (88.6%), and interaction with doctor (89.8%) are good. Conclusion: The end-users’ intentions to use MyOnlineClinic system were particularly influenced by FCs such as hardware, software, and the information technology knowledge/familiarity of users. These factors may get further accentuated when these systems are deployed across countries with different languages, technological infrastructures, and medicolegal environments. Therefore, cloud-based mHealth would help in removing some barriers, such as differences in software versions and interoperability problems of systems at physician and patient ends.


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