Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T04:51:20.644Z Has data issue: false hasContentIssue false

DETERMINANTS OF THE INTENTION TO USE TELEMEDICINE: EVIDENCE FROM PRIMARY CARE PHYSICIANS

Published online by Cambridge University Press:  29 July 2016

Francesc Saigi-Rubió
Affiliation:
Open University of [email protected]
Ana Jiménez-Zarco
Affiliation:
Open University of Catalonia
Joan Torrent-Sellens
Affiliation:
Open University of Catalonia

Abstract

Objectives: While most studies have focused on analyzing the results of telemedicine use, it is crucial to consider the determinants of its use to fully understand the issue. This article aims to provide evidence on the determinants of telemedicine use in clinical practice.

Methods: The survey targeted a total population of 398 medical professionals from a healthcare institution in Spain. The study sample was formed by the ninety-three primary care physicians who responded. Using an extended Technology Acceptance Model and microdata for the ninety-six physicians, binary logistic regression analysis was carried out.

Results: The analysis performed confirmed the model's goodness-of-fit, which allowed 48.1 percent of the dependent variable's variance to be explained. The outcomes revealed that the physicians at the healthcare institution placed greater importance on telemedicine's potential to reduce costs, and on its usefulness to the medical profession. The perception of medical information security and confidentiality and the patients’ predisposition toward telemedicine were the second explanatory factors in order of importance. A third set of moderating effects would appear to corroborate the importance of the physicians’ own opinions.

Conclusions: These results have revealed the need for a dynamic approach to the design of telemedicine use, especially when it targets a variety of end-users. Hence, the importance of conducting studies before using telemedicine, and attempting to identify which of the above-mentioned predictors exert an influence and how.

Type
Assessments
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Chaudhry, B, Wang, J, Wu, S, et al. Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144:742-752.CrossRefGoogle ScholarPubMed
2. Broens, TH, Huis in't Veld, RM, Vollenbroek Hutten, MM, et al. Determinants of successful telemedicine implementations: A literature study. J Telemed Telecare. 2007;13:303-309.CrossRefGoogle ScholarPubMed
3. Roig, F, F, Saigí. Difficulties of incorporating telemedicine in health organizations: Analytical perspectives. Gac Sanit. 2009;23:147.e1-e4.Google Scholar
4. Masters, K. For what purpose and reasons do doctors use the Internet: A systematic review. Int J Med Inform. 2008;77:4-16.Google Scholar
5. Gagnon, MP, Pluye, P, Desmaris, M, et al. A systematic review of interventions promoting clinical information retrieval technology (CIRIT) adoption by healthcare professionals. Int J Med Inform. 2010;79:669-680.CrossRefGoogle Scholar
6. Lluch, M. Healthcare professional's organisational barriers to health information technologies: A review literature. Int J Med Inform. 2011;80:849-862.Google Scholar
7. Kluge, E-HW. Ethical and legal changes for health telematics in a global world: Telehealth and the technological imperative. Int J Med Inform. 2011;80:e1-e5.CrossRefGoogle Scholar
8. Viitanen, J, Hyppönen, H, Lääveri, T, et al. National questionnaire on clinical ICT systems proofs: Physicians suffer from poor usability. Int J Med Inform. 2011;80:708-725.Google Scholar
9. Davis, FD, Bagozzi, RP, Warshaw, PR. User acceptance of computer technology. Manage Sci. 1989;35:982-1003.CrossRefGoogle Scholar
10. Davis, FD. User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. Int J Man Mach Stud. 1993;38:475-487.Google Scholar
11. Lee, Y, Kozar, KA, Larsen, KRT. The technology acceptance model: Past, present and future. Comun Assoc Inform Syst. 2003;12:752-780.Google Scholar
12. Wu, S, Chaudhry, B, Wang, J, et al. Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144:742-752.Google Scholar
13. Jennett, PA, Hall, A, Hailey, D, et al. The socio-economic impact of telehealth: A systematic review. J Telemed Telecare. 2003;9:311-320.CrossRefGoogle ScholarPubMed
14. Bagozzi, RP. The legacy of the technology acceptance model and a proposal for a paradigm shift. J Assoc Inf Syst. 2007;8/4:244-254.Google Scholar
15. Fitgeral, G, Piris, L, Serrano, A. Identification of benefits and barriers for the adoption of E-health information systems using a socio-technical approach. Proceedings of the ITI 30th Int. Conf. on Information Technology Interfaces, June 23–26, 2008, Cavtat, Croatia. pp. 601–606.Google Scholar
16. Schepers, J, Wetzels, M. A meta-analysis of the technology acceptance model: Investigations subjective norm and moderation effects. Inf Manage. 2007;44:90-103.CrossRefGoogle Scholar
17. Limayem, M, Khalifa, K, Frini, A. What makes consumers buy from Internet? A longitudinal study of online shopping. IEEE Trans Syst Man Cybern A Syst Hum. 2000;30:421-432.Google Scholar
18. Robinson, E. E-health and the Internet: Factors that Influence Doctors' Mediation Behaviors with Patients. Communication Theses. Paper 46, 2009.Google Scholar
19. Parasuraman, A, Grewal, D. The impact of technology on the quality-value-loyalty chain: A research agenda. J Acad Mark Sci. 2000;28:168-174.Google Scholar
20. Deutskens, E, de Ruyter, K, Wetzels, M, Oosterveld, P. Response rate and response quality of internet-based surveys: An experimental study. Mark Lett. 2004;15:21-36.Google Scholar
21. Hair, JF, Black, WC, Babin, BJ, Anderson, RE, Tatham, RL. Mutivariate data analysis. Upper Saddle River, NJ. Pearson, Prentice Hall; 2006.Google Scholar
22. de la Torre-Diez, I, Lopez-Coronado, M, Vaca, C, Aguado, J, de Castro, C. Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: A systematic review. Telemed J E Health. 2015;21:81-85.CrossRefGoogle ScholarPubMed
23. Torrent, J. Knowledge products and networks externalities: Implications for the business strategy. J Knowl Econ. 2013;2. doi: 10.1007/s13132-012-0122-7.Google Scholar
24. European Union. e-Health for Europe. Resolving to work together. e-Health conference, “e-Health and e-Health Policies: Synergies for better Health in a Europe of regions”. Conclusions. Malaga, May 10–12 2006.Google Scholar
25. Murray, E, Burns, J, May, C, et al. Why is it difficult to implement e-health initiatives? A qualitative study. Implement Sci 2011;6:6.Google Scholar
Supplementary material: File

Saigi-Rubió et al. supplementary material

Supplementary Appendix tables and figures

Download Saigi-Rubió et al. supplementary material(File)
File 53 KB