An Investigation of Pharmacists’ Acceptance of NHI-PharmaCloud in Taiwan

Journal of Medical Systems - Tập 42 - Trang 1-11 - 2018
Ching-Chang Lee1, Meng-Chi Liu1
1National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan

Tóm tắt

Taiwan’s National Health Insurance (NHI) is one of the most successful insurance programs in the world. The National Health Insurance Administration (NHIA) established the NHI-PharmaCloud as a platform to reduce medication duplication and other medication errors among the NHI-contracted facilities. The NHI-PharmaCloud can help pharmacists access patient medication information from the preceding 3 months to improve drug safety. The use of NHI-PharmaCloud can improve the quality of healthcare, but improvements cannot occur if pharmacists are unwilling to use the platform. Therefore, the main objective of our study is to investigate the factors affecting pharmacists’ adoption of the NHI-PharmaCloud. This study develops a research model using theories of technology adoption, self-efficacy, and perceived risk and uses randomly distributed survey questionnaires to collect data from local pharmacists. The results show that self-efficacy, perceived usefulness, and perceived psychological risk are 3 critical factors that could affect pharmacists’ willingness to use the NHI-PharmaCloud. The research results may also help NHIA to effectively promote the usage of the NHI-PharmaCloud in Taiwan. In addition, governments in other countries may refer to the results of this study when implementing their own PharmaCloud-type systems to improve drug safety.

Tài liệu tham khảo

Ammenwerth, E. et al., A nationwide computerized patient medication history: Evaluation of the Austrian Pilot Project “e-Medikation”. Int J Med Inform. 83(9):655–669, 2014. Rosenthal, A. et al., Cloud computing: A new business paradigm for Biomedical information sharing. J Biomed Inform. 43(2):342–353, 2010. My Health Bank can promote self-health mangement, Ministry of Health and Welfare News website. https://www.nhi.gov.tw/News_Content.aspx?n=FC05EB85BD57C709&sms=587F1A3D9A03E2AD&s=5AFC4D64B1494D67, Published March 1, 2016. Ferner, R. E., and Aronson, J. K., Clarification of terminology in medication errors. Drug safety. 29(11):1011–1022, 2006. World-Health-Organization, Reporting and learning systems for medication errors: the role of pharmacovigilance centres. Geneva, Switzerland: World Health Organization, 2014. The drug-relief report, Taiwan-Drug-Relief-Foundation website. http://www.tdrf.org.tw/files/files/1_until%20may.pdf, Published may 1, 2016. Agrawal, A., Medication errors: prevention using information technology systems. Brit J Clin Pharmaco. 67(6):681–686, 2009. Hsieh, P.J., Lai, H.M., and Hong, Y.L. Explaining Physicians’ Acceptance and Resistance to the NHI Pharmacloud: A Theoretical Model and Empirical Test. in PACIS, 2015. Huang, S. K. et al., NHI-PharmaCloud in Taiwan—A preliminary evaluation using the RE-AIM framework and lessons learned. Int J Med Inform. 84(10):817–825, 2015. Hsieh, P. J., and Lin, W.S., Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model. Inf. Manag. 55(1):51–63, 2018. Kuo, A., Opportunities and Challenges of Cloud Computing to Improve Health Care Services. J Med Internet Res 13(3):e67, 2011. Nur, F. N., and Moon, N. N., Health care system based on cloud computing. Asian Transactions on Computers. 2(5):9–11, 2012. Mathew, S., Cloud computing: a new foundation towards health care. International Journal of Innovative Technology and Exploring Engineering. 3(2):118–121, 2013. Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3):319–340, 1989. Hu, L. T., and Bentler, P. M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 6(1):1–55, 1999. Venkatesh, V., and Davis, F. D., A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manag. Sci. 46(2):186–204, 2000. Wu, L., Li, J. Y., and Fu, C. Y., The adoption of mobile healthcare by hospital's professionals: An integrative perspective. Decis Support Syst. 51(3):587–596, 2011. Fishbein, M., and Ajzen, I., Belief, attitude, intention and behaviour: An introduction to theory and research. Boston, USA: Addison-Wesley, 1975. Karsh, B. T., Escoto, K. H., Beasley, J. W., and Holden, R. J., Toward a theoretical approach to medical error reporting system research and design. Appl Ergon. 37(3):283–295, 2006. Mathieson, K., Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inform Syst Res. 2(3):173–191, 1991. Taylor, S., and Todd, P. A., Understanding information technology usage: A test of competing models. Inform Syst Res. 6(2):144–176, 1995. Gavaza, P. et al., Examination of pharmacists’ intention to report serious adverse drug events (ADEs) to the FDA using the theory of planned behavior. Research in Social and Administrative Pharmacy. 7(4):369–382, 2011. Moore, G. C., and Benbasat, I., Development of an instrument to measure the perceptions of adopting an information technology innovation. Inform Syst Res. 2(3):192–222, 1991. Pfeffer, J., Organizations and organization theory. Boston: Pitman, 1982. Makowsky, M. J., Guirguis, L. M., Hughes, C. A., Sadowski, C. A., and Yuksel, N., Factors influencing pharmacists’ adoption of prescribing: qualitative application of the diffusion of innovations theory. Implement Sci. 8(1):1, 2013. Bandura, A., Social foundations of thought and action: A social cognitive theory. Upper Saddle River: Prentice-Hall Inc, 1986. Amin, H., Internet banking adoption among young intellectuals. The Journal of Internet Banking and Commerce. 12(3):1–13, 2007. Chan, S. C., and Lu, M. T., Understanding internet banking adoption and use behavior: A Hong Kong perspective. J Glob Inf Manag. 12(3):21–43, 2004. Compeau, D. R., Higgins, C. A., and Huff, S., Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Q. 23(2):145–158, 1999. Compeau, D. R., and Higgins, C. A., Computer self-efficacy: Development of a measure and initial test. MIS Q. 19(2):189–211, 1995. Hasan, B., Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Inform Manage. 43(5):565–571, 2006. Bandura, A., Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 84(2):191, 1977. Bandura, A., Health promotion by social cognitive means. Health Educ Behav. 31(2):143–164, 2004. Rimal, R. N., Perceived risk and self-efficacy as motivators: Understanding individuals' long-term use of health information. J Commun. 51(4):633–654, 2001. Gist, M. E., and Mitchell, T. R., Self-efficacy: A theoretical analysis of its determinants and malleability. Acad Manage Rev. 17(2):183–211, 1992. Wang, Y. S., Wang, Y. M., Lin, H. H., and Tang, T. I., Determinants of user acceptance of Internet banking: an empirical study. Int J Serv Ind Manag. 14(5):501–519, 2003. Bauer, R. A., Consumer behavior and risk taking in risk taking and information handling in consumer behavior, Edited by: Donald F. Cox. Cambride, USA: Harvard University Press, 1967. Dowling, G. R., and Staelin, R., A model of perceived risk and intended risk-handling activity. J Consum Res. 21(1):119–134, 1994. Igbaria, M., User acceptance of microcomputer technology: an empirical test. Omega. 21(1):73–90, 1993. Carroll, N. V., Siridhara, C., and Fincham, J. E., Perceived risks and pharmacists' generic substitution behavior. J Consum Aff. 20(1):36–47, 1986. Cunningham, S. M., The major dimensions of perceived risk. Risk taking and information Handling in consumer behavior. 1:82–111, 1967. Peterson, G., Wu, M., and Bergin, J., Pharmacists’ attitudes towards dispensing errors: their causes and prevention. J Clin Pharm Ther. 24(1):57–71, 1999. Aspden, P., Corrigan, J. M., Wolcott, J., and Erickson, S. M., Patient safety: achieving a new standard for care. Washington, DC, USA: National Academies Press, 2004. Featherman, M. S., and Pavlou, P. A., Predicting e-services adoption: a perceived risk facets perspective. Int J Hum-Compu St. 59(4):451–474, 2003. Salahuddin, L., and Ismail, Z., Classification of antecedents towards safety use of health information technology: A systematic review. Int J Med Inform. 84(11):877–891, 2015. Nunnally, J., Psychometric methods. New York: McGraw-Hill, 1978. Anderson, J. C., and Gerbing, D. W., Structural equation modeling in practice: A review and recommended two-step approach. Psycho Bull. 103(3):411, 1988. Fornell, C., and Bookstein, F. L., Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J. Mark. Res. 19(4):440–452, 1982. Seyal, A. H., Rahman, M. N. A., and Rahim, M. M., Determinants of academic use of the Internet: a structural equation model. Behav Inform Technol. 21(1):71–86, 2002. Hair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C., Multivariate data analysis, 5th. New York: Prentice Hall International, 1998.