Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective

The healthcare sector is growing rapidly in developing electronic personalize health records (e-PHR) as an internet-based eHealth implementation in many countries. The ePHR has recognized as one of the important roles in managing personalize health and health informatics. However, the acceptance of...

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Main Author: Mohd Yaacob, Noorayisahbe
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institution Universiti Teknikal Malaysia Melaka
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advisor Hasan Basari, Abd. Samad

topic R Medicine (General)
spellingShingle R Medicine (General)
Mohd Yaacob, Noorayisahbe
Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective
description The healthcare sector is growing rapidly in developing electronic personalize health records (e-PHR) as an internet-based eHealth implementation in many countries. The ePHR has recognized as one of the important roles in managing personalize health and health informatics. However, the acceptance of e-PHRs in Peninsular Malaysia is still under study. The lack of user acceptance is a critical impediment to the success of an information system (IS) model. Thus, understanding an individual’s acceptance or rejection of information technology (IT) is considered as one of the most challenging issues. Most of the primary care sector, both public and private hospitals including clinics in Malaysia are still ambiguous with e-PHR. Therefore, the need to conduct a research on the factors that influence and how e-PHR can be accepted especially for Malaysian patients, physicians and organizations are very essential. The aim of this study is to investigate the factors that are important in order to propose an e-PHR acceptance model from patients, physicians and organizations perspectives within the context of Malaysian public and private hospitals including clinics. Accordingly, this study integrated the Unified Theory of Acceptance and Use of Technology (UTAUT2), Diffusion of Innovations (DOI), Technology Organization Environment (TOE) and Cultural (Hofstede) to propose the factors influencing acceptance of the e-PHR in Peninsular Malaysia. This research utilized the mix method approach which are triangulation technique and interviews including a survey through a web-based questionnaire that involved seventy-one dimensions for patients, eighty-four dimensions for physicians and about ninety-four dimensions for the organization. Twenty-four hypotheses have been determined to test the proposed model. The analysis has been done through SPSS and SmartPLS software to evaluate internal consistency, indicator reliability, convergent and discriminant validity of the survey instrument. The results from the initial hypotheses testing indicated that personal innovativeness, knowledge, privacy and security, and trust are the most significant factors for the acceptance of e-PHR based on Peninsular Malaysia patients, physicians and organizations with Average Variance Extracted (AVE) > 0.05. The results from moderation hypothesis testing shown that there is no significant effect with AVE <0.05 on the relationships in the model. The proposed model has been validated by ten selected experts from different organizations. This study concluded that there is an existence of significant factors on e-PHR acceptance among patients, physicians and organizations. The research findings conclude that the significant factors have the same effect on e-PHR acceptance within Peninsular Malaysia patients, physicians and organizations. This research has contributed to the body of knowledge in the field of health informatics which focusing on e-PHR acceptance in Peninsular Malaysia.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Mohd Yaacob, Noorayisahbe
author_facet Mohd Yaacob, Noorayisahbe
author_sort Mohd Yaacob, Noorayisahbe
title Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective
title_short Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective
title_full Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective
title_fullStr Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective
title_full_unstemmed Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective
title_sort acceptance model for electronic personalized health records in peninsular malaysia based on users perspective
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty of Information and Communication Technology
publishDate 2020
url http://eprints.utem.edu.my/id/eprint/25391/1/Acceptance%20Model%20For%20Electronic%20Personalized%20Health%20Records%20In%20Peninsular%20Malaysia%20Based%20On%20Users%20Perspective.pdf
http://eprints.utem.edu.my/id/eprint/25391/2/Acceptance%20Model%20For%20Electronic%20Personalized%20Health%20Records%20In%20Peninsular%20Malaysia%20Based%20On%20Users%20Perspective.pdf
_version_ 1747834116388159488
spelling my-utem-ep.253912021-11-17T08:39:40Z Acceptance Model For Electronic Personalized Health Records In Peninsular Malaysia Based On Users Perspective 2020 Mohd Yaacob, Noorayisahbe R Medicine (General) The healthcare sector is growing rapidly in developing electronic personalize health records (e-PHR) as an internet-based eHealth implementation in many countries. The ePHR has recognized as one of the important roles in managing personalize health and health informatics. However, the acceptance of e-PHRs in Peninsular Malaysia is still under study. The lack of user acceptance is a critical impediment to the success of an information system (IS) model. Thus, understanding an individual’s acceptance or rejection of information technology (IT) is considered as one of the most challenging issues. Most of the primary care sector, both public and private hospitals including clinics in Malaysia are still ambiguous with e-PHR. Therefore, the need to conduct a research on the factors that influence and how e-PHR can be accepted especially for Malaysian patients, physicians and organizations are very essential. The aim of this study is to investigate the factors that are important in order to propose an e-PHR acceptance model from patients, physicians and organizations perspectives within the context of Malaysian public and private hospitals including clinics. Accordingly, this study integrated the Unified Theory of Acceptance and Use of Technology (UTAUT2), Diffusion of Innovations (DOI), Technology Organization Environment (TOE) and Cultural (Hofstede) to propose the factors influencing acceptance of the e-PHR in Peninsular Malaysia. This research utilized the mix method approach which are triangulation technique and interviews including a survey through a web-based questionnaire that involved seventy-one dimensions for patients, eighty-four dimensions for physicians and about ninety-four dimensions for the organization. Twenty-four hypotheses have been determined to test the proposed model. The analysis has been done through SPSS and SmartPLS software to evaluate internal consistency, indicator reliability, convergent and discriminant validity of the survey instrument. The results from the initial hypotheses testing indicated that personal innovativeness, knowledge, privacy and security, and trust are the most significant factors for the acceptance of e-PHR based on Peninsular Malaysia patients, physicians and organizations with Average Variance Extracted (AVE) > 0.05. The results from moderation hypothesis testing shown that there is no significant effect with AVE <0.05 on the relationships in the model. The proposed model has been validated by ten selected experts from different organizations. This study concluded that there is an existence of significant factors on e-PHR acceptance among patients, physicians and organizations. The research findings conclude that the significant factors have the same effect on e-PHR acceptance within Peninsular Malaysia patients, physicians and organizations. This research has contributed to the body of knowledge in the field of health informatics which focusing on e-PHR acceptance in Peninsular Malaysia. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25391/ http://eprints.utem.edu.my/id/eprint/25391/1/Acceptance%20Model%20For%20Electronic%20Personalized%20Health%20Records%20In%20Peninsular%20Malaysia%20Based%20On%20Users%20Perspective.pdf text en validuser http://eprints.utem.edu.my/id/eprint/25391/2/Acceptance%20Model%20For%20Electronic%20Personalized%20Health%20Records%20In%20Peninsular%20Malaysia%20Based%20On%20Users%20Perspective.pdf text en public https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119784 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Hasan Basari, Abd. Samad 1. Abidi, S.S. and Goh, a, 2000. A personalised Healthcare Information Delivery System: pushing customised healthcare information over the WWW. Studies in health technology and informatics, 77(i), pp.663–667. Available at: http://www.ncbi.nlm.nih.gov/pubmed/11187636. 2. Abo-Hamad, W. and Arisha, A., 2012. Multi-criteria framework for emergency department in Irish hospital. Proceedings - Winter Simulation Conference. 3. Abualbasal, W., Abu-Shanab, E. and Al-Quraan, H., 2016. Dynamic Analysis of UTAUT. International Journal of Web-Based Learning and Teaching Technologies, 11(3), pp.40–54. Available at: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWLTT.2016070104. 4. Agrawal, E., 2010. Acceptance and Use of Personal Health Record: Factors Affecting Physicians’ Perspective. Indiana University, December, p.113. 5. Ahmadi, H., Ibrahim, O. and Nilashi, M., 2015. Journal of Soft Computing and Decision Support Systems Investigating a New Framework for Hospital Information System Adoption: A Case on Malaysia. , 2(2), pp.26–33. 6. Aizstrauta, D., Ginters, E. and Eroles, M.A.P., 2015. Applying theory of diffusion of innovations to evaluate technology acceptance and sustainability. Procedia Computer Science. 2015 7. Alazzam, M.B., 2015. EHRS acceptance in Jordan hospitals by UTAUT2 model: Preliminary result. Journal of Theoretical and Applied Information Technology, 78(3),pp.473–482. 8. Almunawar, M.N. and Anshari, M., 2012. Health Information Systems (HIS): Concept and Technology. , (March). Available at: http://arxiv.org/abs/1203.3923. 9. Amadi-obi, A., 2014. Telemedicine in pre-hospital care: a review of telemedicine applications in the pre-hospital environment. International Journal of Emergency Medicine, 7(1), p.29. Available at: http://www.intjem.com/content/7/1/29 [Accessed: 11 October 2014]. 10. Andrews, L., Gajanayake, R. and Sahama, T., 2014. The Australian general public’s perceptions of having a personally controlled electronic health record (PCEHR). International Journal of Medical Informatics, 83(12), pp.889–900. 11. Ant Ozok, 2013. Usability and perceived usefulness of personal health records for preventive health care: A case study focusing on patients’ and primary care providers’ perspectives. Applied ergonomics, 45(3), pp.613–628. Available at: http://dx.doi.org/10.1016/j.apergo.2013.09.005 [Accessed: 24 January 2014]. 12. Archer, N., 2011. Personal health records: a scoping review. Journal of the American Medical Informatics Association : JAMIA, 18(4), pp.515–22. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3128401&tool=pmcentrez&rendertype=abstract [Accessed: 3 August 2014]. 13. Assadi, V. and Hassanein, K., 2017. Consumer Adoption of Personal Health Record Systems: A Self-Determination Theory Perspective. Journal of medical Internet research, 19(7), p.e270. 14. Awa, H.O., Ojiabo, O.U. and Emecheta, B.C., 2015. Integrating TAM, TPB and TOE 15. frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science and Technology Policy Management. 16. Awa, H.O., Ukoha, O. and Igwe, S.R., 2017. Revisiting technology-organization-environment (T-O-E) theory for enriched applicability. Bottom Line. 17. Baird, A., North, F. and Raghu, T.S., 2011. Personal Health Records (PHR) and the future of the physician-patient relationship. Proceedings of the 2011 iConference on - iConference ’11. 2011 pp. 281–288. 18. Baker, J., 2011. The Technology–Organization–Environment Framework. 19. Baldwin, J.L., Singh, H., Sittig, D.F. and Giardina, T.D., 2017. Patient portals and health apps: Pitfalls, promises, and what one might learn from the other. Healthcare. 20. Baptista, G. and Oliveira, T., 2015. Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50(September), pp.418–430. 21. Barnes, P.J., 2019. Chronic obstructive pulmonary disease. In: Genomic and Precision Medicine: Infectious and Inflammatory Disease. 22. Bartholomew, K.W., 2016. Patient Portals: Achieving Technology Acceptance and Meaningful Use in Independent Physician-Managed Practices. 23. Bash, E., Mouton, J.H.M., Sapsford, R. and Jupp, V., 2006. Data Collection and Analysis, 24. Ben-Assuli, O., 2015. Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments. Health policy (Amsterdam, Netherlands), 119(3), pp.287–97. Available at:http://www.sciencedirect.com/science/article/pii/S0168851014003297. 25. Bentahar, O. and Cameron, R., 2015. Design and implementation of a mixed method research study in project management. Electronic Journal Of Business Research Methods. 26. Bigdeil, A.Z., Kamal, M. and de Cesare, S., 2013. Information sharing through inter-organisational systems in local government. Transforming Government: People, Process and Policy, 7(2), pp.148–176. Available at: http://search.proquest.com/docview/1355460145?accountid=17193%5Cnhttp://sfx.brad.ac.uk/sfx_local?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&genre=article&sid=ProQ:ProQ:abiglobal&atitle=Information+sharing+through+inter-organisational+sys. 27. Bollard, S.S. and Browder, J.S., 2017. Sampling. In: Practical Handbook of Spectroscopy. 28. Bouayad, L., Ialynytchev, A. and Padmanabhan, B., 2017. Patient health record systems scope and functionalities: Literature review and future directions. Journal of Medical Internet Research. 29. Brandt, R. and Rice, R., 2014. Building a better PHR paradigm: Lessons from the discontinuation of google healthTM. Health Policy and Technology, 3(3), pp.200–207. 30. Broadhurst, K., Holt, K. and Doherty, P., 2012. Accomplishing parental engagement in child protection practice?: A qualitative analysis of parent-professional interaction in pre-proceedings work under the Public Law Outline. Qualitative Social Work, 11(5), pp.517–534. 31. Bunker, E., 2017. Development of a tripolar model of technology acceptance: Hospital-based physicians’ perspective on EHR. International Journal of Medical Informatics. 32. Cahill, J.E., Gilbert, M.R. and Armstrong, T.S., 2014. Personal health records as portal to the electronic medical record. Journal of Neuro-Oncology. 33. Capello, F. and Luini, M.G.G., 2014. eHealth policy. In: eHealth, Care and Quality of Life. 34. Cappiello, M.M., 2011. NIH Public Access. Journal of Ethnopharmacology. 35. Chandramohan, D., 2015. A multi-agent approach: To preserve user information privacy for a pervasive and ubiquitous environment. Egyptian Informatics Journal, 16(1), pp.151–166. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1110866515000067 [Accessed: 27 April 2015]. 36. Chang, F. and Gupta, N., 2015. Progress in electronic medical record adoption in Canada. Canadian Family Physician, 61(12), pp.1076–1084. 37. Chang, H.C., 2010. A new perspective on Twitter hashtag use: Diffusion of innovation theory. Proceedings of the ASIST Annual Meeting, 47. 38. Chen, T.S., 2012. Secure dynamic access control scheme of PHR in cloud computing. Journal of Medical Systems. 2012 pp. 4005–4020. 39. Chuang, B.-K., 2016. A Study of Personal Health Record User’s Behavioral Model Based on the PMT and UTAUT Integrative Perspective. International Journal of Environmental Research and Public Health, 14(1), p.8. 40. Copetti, A., Leite, J.C.B., Loques, O. and Fritsch, M., 2013. A decision-making mechanism for context inference in pervasive healthcare environments. Decision Support Systems, 55(2), pp.528–537. Available at: http://dx.doi.org/10.1016/j.dss.2012.10.010. 41. Cornetta, K. and Brown, C.G., 2013. Balancing Personalized Medicine and Personalized Care. Academic Medicine, 88(3), pp.309–313. Available at: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00001888-201303000-00014. 42. Creswell, J.W., 2014. w Creswell, J. (2009). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications, Incorporated., 43. Creswell, J.W., 2013. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 44. Cruickshank, J., Packman, C. and Paxman, J., 2012. Putting patients in control ? Personal Health Records. 2020Health.Org, (September), pp.1–55. 45. Dalal, A.K., 2016. A web-based, patient-centered toolkit to engage patients and caregivers in the acute care setting: A preliminary evaluation. Journal of the American Medical Informatics Association, 23(1), pp.80–87. 46. Delmastro, F., 2012. Pervasive communications in healthcare. Computer Communications, 35(11), pp.1284–1295. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0140366412001430 [Accessed: 30 October 2014]. 47. Denzin, N.K., 2012. Triangulation 2.0*. Journal of Mixed Methods Research. 48. Department of Statistics Malaysia, 2019. Demographic Statistics First Quarter 2019, Malaysia. Available at: https://dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=430&bul_id=Mno2WGQ3QUdmaUM3c3l0NzN0aW9tZz09&menu_id=L0pheU43NWJwRWVSZklWdzQ4TlhUUT09. 49. Dey, A.K. and Estrin, D., 2011. Pervasive healthcare 2010: Two perspectives. IEEE Pervasive Computing, 10(3), pp.8–11. 50. Dudel, C. and Klüsener, S., 2016. Estimating male fertility in eastern and western Germany since 1991: A new lowest low? Demographic Research, 35(1), pp.1549–1560. 51. Dutta, B., Peng, M.H. and Sun, S.L., 2018. Modeling the adoption of personal health record (PHR) among individual: the effect of health-care technology self-efficacy and gender concern. Libyan Journal of Medicine. 52. El-Yafouri, R. and Klieb, L., 2015. Electronic medical records adoption and use: Understanding the barriers and the levels of adoption for physicians in the USA. 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services, Healthcom 2014. 2015 pp. 506–512. 53. Fazilah, 2014. NATIONAL eHEALTH: “Moving Towards Efficient Healthcare.” MOH. 54. Fêng, H.-Y., 2014. Definition of Terms. The Chinese Kinship System, pp.2–2. 55. French, M., 2014. Gaps in the gaze: Informatic practice and the work of public health surveillance. Surveillance and Society, 12(2), pp.226–243. 56. Fry, C.L., Spriggs, M., Arnold, M. and Pearce, C., 2014. Unresolved Ethical Challenges for the Australian Personally Controlled Electronic Health Record (PCEHR) System: Key Informant Interview Findings. AJOB Empirical Bioethics, 5(4), pp.30–36. 57. Gagnon, M.-P., 2016. Adoption of Electronic Personal Health Records in Canada: Perceptions of Stakeholders. International Journal of Health Policy and Management, 5(7), pp.425–433. Available at: http://ijhpm.com/article_3180_629.html. 58. Gagnon, M., 2016. Original Article Adoption of Electronic Personal Health Records in Canada : Perceptions of Stakeholders. Kerman University of Medical Sciences, 5(7), pp.425–433. Available at: http://dx.doi.org/10.15171/ijhpm.2016.36. 59. Gangwar, H., Date, H. and Ramaswamy, R., 2015. Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management. 60. Gao, Y., Li, H. and Luo, Y., 2015. An empirical study of wearable technology acceptance in healthcare. Industrial Management and Data Systems. 61. Gartrell, Kyungsook, 2015. Electronic personal health record use among registered nurses. Nursing outlook, 63(3), pp.278–87. Available at: http://www.sciencedirect.com/science/article/pii/S0029655414002711. 62. Gartrell, K., 2015. Testing the Electronic Personal Health Record Acceptance Model by Nurses for Managing Their Own Health. Applied Clinical Informatics, 6(2), pp.224–247. Available at: http://www.schattauer.de/index.php?id=1214&doi=10.4338/ACI-2014-11-RA-0107. 63. Georgescu, I., Lightbody, G., Galway, L. and Mccullagh, P., 2014. Pervasive Health. Nature Physics, 8(6), pp.1–17. Available at: http://link.springer.com/10.1007/978-1-4471-6413-5. 64. Ghani, M.K.A., 2010. AN INTEGRATED AND DISTRIBUTED FRAMEWORK FOR A MALAYSIAN TELEMEDICINE SYSTEM (MyTel). 65. Ghani, M.K.A., Bali, R.K., Naguib, R.N. and Marshall, I.M., 2013. Pervasive Health Knowledge Management. In: Pervasive Health Knowledge Management. pp. 81–101. 66. Gianetta Feistel, 2014. TECHNOLOGY ACCEPTANCE MODEL : FACTORS INFLUENCING CONSUMERS ’ INTENT TO USE ELECTRONIC PERSONAL HEALTH RECORDS Gianetta Feistel A journal article submitted in partial fulfillment of the requirements for the degree of Doctor of Health Administration School. , (January), p.166. 67. Ginty, A.T., 2013. Construct Validity. In: Gellman, M.D. and Turner, J.R., (eds.) Encyclopedia of Behavioral Medicine. Springer New York, New York, NY, p. 487. 68. Glaser, J., Henley, D.E., Downing, G. and Brinner, K.M., 2008. Advancing personalized health care through health information technology: an update from the American Health Information Community’s Personalized Health Care Workgroup. J Am Med Inform Assoc, 15(4), pp.391–396. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18436899. 69. Gope, P. and Amin, R., 2016. A Novel Reference Security Model with the Situation Based Access Policy for Accessing EPHR Data. Journal of Medical Systems, 40(11). 70. Gregory, M. and Tembo, S., 2017. Implementation of E-health in Developing Countries Challenges and Opportunities: A Case of Zambia. Science and Technology, 7(2), pp.41–53. 71. Griebel, L., 2015. A scoping review of cloud computing in healthcare. BMC Medical Informatics and Decision Making. 72. Grinspan, Z.M., Banerjee, S., Kaushal, R. and Kern, L.M., 2013. Physician Specialty and Variations in Adoption of Electronic Health Records. Applied Clinical Informatics, 4(2), pp.225–240. Available at:http://www.schattauer.de/index.php?id=1214&doi=10.4338/ACI-2013-02-RA-0015. 73. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M., 2013. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. Sage. 74. Hair, J.F.J., Sarstedt, M., Ringle, C.M. and Gudergan, S.P., 2017. Advanced Issues in Partial Least Squares Structural Equation Modeling, 75. Hall, A.K., Bernhardt, J.M. and Dodd, V., 2015. Older Adults Use of Online and Offline Sources of Health Information and Constructs of Reliance and Self-Efficacy for Medical Decision Making. Journal of Health Communication. 76. Haluza, D. and Jungwirth, D., 2015. ICT and the future of health care: Aspects of health promotion. International Journal of Medical Informatics, 84(1), pp.48–57. 77. Hameed, M.A., Counsell, S. and Swift, S., 2012. A conceptual model for the process of IT innovation adoption in organizations. Journal of Engineering and Technology Management - JET-M. 78. Hart, M., 2007. Data Collection. International Journal of Childbirth Education, 22(3), pp.27–31. Available at: http://search.ebscohost.com/login.aspx?direct=true&db=awh&AN=27149258&site=ehost-live. 79. Heart, T., Ben-Assuli, O. and Shabtai, I., 2017. A review of PHR, EMR and EHR integration: A more personalized healthcare and public health policy. Health Policy and Technology, 6(1), pp.20–25. 80. Heath, M. and Porter, T.H., 2017. Patient health records: An exploratory study of patient satisfaction. Health Policy and Technology. 81. Hermiyanty, Wandira Ayu Bertin, D.S., 2017. Sampling Methods in Northwest Coast Household Archaeology: A Simulation Approach Using Faunal Data from the Ozette Site. Journal of Chemical Information and Modeling, 8(9), pp.1–58. 82. Hoffman, M.A. and Williams, M.S., 2011. Electronic medical records and personalized medicine. Hum Genet, 130(1), pp.33–39. 83. Hoque, M.R., Bao, Y. and Sorwar, G., 2016. Investigating factors influencing the adoption of e-Health in developing countries : A patient ’ s perspective. , 8157(February), pp.0–17. 84. Hossain, M.S., 2011. Adaptive media service framework for health monitoring. Proceedings of the Third International Conference on Internet Multimedia Computing and Service - ICIMCS ’11, p.70. Available at: http://dl.acm.org/citation.cfm?doid=2043674.2043694. 85. Huang, C.-Y. and Kao, Y.-S., 2015. UTAUT2 Based Predictions of Factors Influencing the Technology Acceptance of Phablets by DNP. Mathematical Problems in Engineering. 86. Huba, N. and Zhang, Y., 2012. Designing patient-centered personal health records (PHRs): Health care professionals’ perspective on patient-generated data. Journal of Medical Systems. 2012 pp. 3893–3905. 87. Irizarry, T., 2017. Patient Portals as a Tool for Health Care Engagement: A Mixed-Method Study of Older Adults With Varying Levels of Health Literacy and Prior Patient Portal Use. Journal of medical Internet research. 88. Jaafar, S., 2013. Malaysia Health System Review. Health Systems in Transition, 3(1),pp.1–103. Available at: http://www.wpro.who.int/asia_pacific_observatory/hits/series/Malaysia_Health_Systems_Review2013.pdf. 89. Juntunen, J., 2017. Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study. Journal of Medical Internet Research, 19(12), p.e429. 90. Katz, L.B., Grady, M., Stewart, L. and Cameron, H., 2016. Patient and healthcare professional satisfaction with a new, high accuracy blood glucose meter with color range indicator and wireless connectivity. Expert Review of Medical Devices. 91. Kaufman, D.R., 2003. Usability in the real world: Assessing medical information technologies in patients’ homes. Journal of Biomedical Informatics, 36(1–2), pp.45–60. 92. Kazemi, H., 2015. 350mW G-band medium power amplifier fabricated through a new method of 3D-copper additive manufacturing. 2015 IEEE MTT-S International Microwave Symposium, IMS 2015, 36(1), pp.157–178. 93. Kerns, J.W., 2013. How patients want to engage with their personal health record: A qualitative study. BMJ Open. 94. Ketikidis, P., Dimitrovski, T., Lazuras, L. and Bath, P.A., 2012. Acceptance of health information technology in health professionals: An application of the revised technology acceptance model. Health Informatics Journal. 2012 95. Khan, I.A., 2015. Personalized Electronic Health Record System for Monitoring Patients with Chronic Disease. , pp.121–126. 96. Kim, K., 2012. Benefits of and barriers to the use of personal health records (PHR) for health management among adults. Online Journal of Nursing Informatics. 97. Kim, M.I. and Johnson, K.B., 2002. Personal health records: Evaluation of functionality and utility. Journal of the American Medical Informatics Association, 9(2), pp.171–180. 98. Kit, A.H.L., Ni, A.H., Badri, E.N.F.B.M. and Yee, T.K., 2014. UTAUT2 influencing the behavioural intention to adopt mobile applications. Thesis-Bachelor, (May). 99. Kiwanuka, A., 2015. (PDF) Acceptance Process: The Missing Link between UTAUT and Diffusion of Innovation Theory. American Journal of Information Systems,. 100. Kluver, R. and Fu, W., 2018. 15. Measuring Cultural Globalization in Southeast Asia. Globalization and Its Counter-forces in Southeast Asia, (October), pp.335–358. 101. Kowsalya, M., Thamilmaran, A. and Vijayapriya, P., 2017. Supervisor control for a stand-alone hybrid generation system. International Journal of Applied Engineering Research, 12(14), pp.4090–4097. 102. Kyriazis, D., 2017. CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health. Informatics Empowers Healthcare Transformation, 238, p.19. 103. Lai, E. and Friedl, K.E., 2015. Digital Soldiers : Transforming Personalized Health in Challenging and Changing Environments. 104. Lai, P.C., 2017. the Literature Review of Technology Adoption Models and Theories for the Novelty Technology. Journal of Information Systems and Technology Management, 14(1), pp.21–38. Available at:http://www.jistem.fea.usp.br/index.php/jistem/article/view/10.4301%25S1807-17752017000100002. 105. Lamsaard, J., Pongthananikorn, S. and Theeraroungchaisri, A., 2016. Development of a chronic kidney disease knowledge website with electronic personal health records for patients. Thai Journal of Pharmaceutical Sciences, 40, pp.159–162. 106. Lavariega, J.C., 2016. EEMI - An electronic health record for pediatricians: Adoption barriers, services and use in Mexico. International Journal of Healthcare Information Systems and Informatics, 11(3), pp.57–69. 107. Lee, L.H., Chou, Y.T., Huang, E.W. and Liou, D.M., 2013. Design of a Personal Health Record and Health Knowledge Sharing System using IHE-XDS and OWL. J Med Syst, 37(2), p.9921. Available at: http://www.ncbi.nlm.nih.gov/pubmed/23321976%5Cnhttp://download.springer.com/static/pdf/339/art:10.1007/s10916-012-9921-4.pdf?auth66=1425895266_6429ebca57f7cbfac837064056177e82&ext=.pdf. 108. Leguina, A., 2015. A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education. 109. Leon, N., Schneider, H. and Daviaud, E., 2012. Applying a framework for assessing the health system challenges to scaling up mHealth in South Africa. BMC medical informatics and decision making, 12, p.123. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3534437&tool=pmcentrez&rendertype=abstract. 110. Leyland, M., Archer, N., Deal, K. and Hassanein, K., 2011. Designing a service framework for electronic personal health records: A patient-centred approach. Value in Health, 14(3), p.A105. Available at: http://www.embase.com/search/results?subaction=viewrecord%7B&%7Dfrom=export%7B&%7Did=L70490997%5Cnhttp://sfxhosted.exlibrisgroup.com/medtronic?sid=EMBASE%7B&%7Dissn=10983015%7B&%7Did=doi:%7B&%7Datitle=Designing+a+service+framework+for+electronic+personal+. 111. Li, C., 2013. A new energy framework with distribution descriptors for image segmentation. IEEE Transactions on Image Processing. 112. Li, H., Gupta, A., Zhang, J. and Sarathy, R., 2014. Examining the decision to use standalone personal health record systems as a trust-enabled fair social contract. Decision Support Systems, 57, pp.376–386. Available at: http://dx.doi.org/10.1016/j.dss.2012.10.043. 113. Li, X., Hess, T.J., McNab, A.L. and Yu, Y., 2009. Culture and acceptance of global web sites: a cross-country study of the effects of national cultural values on acceptance of a personal web portal. SIGMIS Database. 114. Liu, C.F., Tsai, Y.C. and Jang, F.L., 2013. Patients’ acceptance towards a web-based personal health record system: An empirical study in Taiwan. International Journal of Environmental Research and Public Health, 10(10), pp.5191–5208. 115. Liu, L.S., Shih, P.C. and Hayes, G.R., 2011. Barriers to the adoption and use of personal health record systems. Proceedings of the 2011 iConference on - iConference ’11, pp.363–370. Available at: http://dl.acm.org/citation.cfm?id=1940761.1940811&coll=portal&dl=GUIDE. 116. Lluch, M., 2011. Healthcare professionals’ organisational barriers to health information technologies-A literature review. International Journal of Medical Informatics, 80(12), pp.849–862. 117. Lober, W.B., 2006. Barriers to the use of a personal health record by an elderly population. AMIA Annual Symposium Proceedings, pp.514–518. 118. Marangunić, N. and Granić, A., 2015. Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society. 119. McGregor, B., 2015. Improving service coordination and reducing mental health disparities through adoption of electronic health records. Psychiatric services (Washington, D.C.), 66(9), pp.985–7. Available at:http://www.ncbi.nlm.nih.gov/pubmed/25975885%5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/25975885. 120. Miadinovic, J. and Xiang, H., 2016. A Study on Factors Affecting the Behavioral Intention to use Mobile Shopping Fashion Apps in Sweden. , pp.1–75. Available at: http://www.diva-portal.org/smash/get/diva2:933382/FULLTEXT01.pdf. 121. Miller, D.M., Moore, S.M., Fox, R.J., Atreja, A., Fu, A.Z., Lee, J.-C., Saupe, W., Stadtler, M., Chakraborty, S., Harris, C.M., 2011. Web-Based Self-Management for Patients with Multiple Sclerosis: A Practical, Randomized Trial. Telemedicine and e-Health, 17(1), pp.5–13. 122. Ministry of Health Malaysia, 2014. NATIONAL eHEALTH: “Moving Towards Efficient Healthcare.” Ministry of Health Malaysia, 2011. Country Health Plan 2011 - 2015, 123. MKA Ghani, MM. Jaber, N.S.H., 2015. Barriers faces telemedicine implementation in the developing countries: Toward building Iraqi telemedicine framework. ARPN Journal of Engineering and Applied Sciences. 124. Molina, K.M., 2013. Encyclopedia of Behavioral Medicine, 125. Montero-Marín, J., 2015. Expectations Among Patients and Health Professionals Regarding Web-Based Interventions for Depression in Primary Care: A Qualitative Study. Journal of Medical Internet Research, 17(3), p.e67. Available at: http://www.jmir.org/2015/3/e67/. 126. Morton, A.A., 2011. Examining acceptance of an integrated personal health record (PHR). Dissertation Abstracts International: Section B: The Sciences and Engineering. 127. Muhammad, I., Teoh, S.Y. and Wickramasinghe, N., 2014. Trying to streamline healthcare delivery in Australia via the personally controlled electronic health record (PCEHR). In: Lean Thinking for Healthcare. pp. 187–206. 128. Musafar Hameed, L.B.B., 2014. An inquiry into privatisation’s impact on healthcare services in Malaysia. , 1, pp.1–17. Available at: http://studentsrepo.um.edu.my/4626/3/Chapter_I_Introduction.pdf. 129. Nadel, M.R., 2013. Assessing screening quality in the CDC’s Colorectal Cancer Screening Demonstration Program. Cancer. 130. Najaftorkaman, M.., Ghapanchi, A.H.. and Talaei-Khoei, A.., 2014. Analysis of research in adoption of person-Centred healthcare systems: The case of online personal health record. Proceedings of the 25th Australasian Conference on Information Systems, ACIS 2014. Available at: https://www.scopus.com/inward/record.uri?eid=2-s2.084959559407&partnerID=40&md5=cde1b3ff9a3f7894a83e4609ef1a6c30. 131. Nambisan, P., 2017. Factors that impact Patient Web Portal Readiness (PWPR) among the underserved. International Journal of Medical Informatics. 132. Nazari, F., Khosravi, F. and Babalhavaeji, F., 2013. Applying Rogers’ diffusion of innovation theory to the acceptance of online databases at university zone of Iran. Malaysian Journal of Library and Information Science. 133. Neubeck, L., 2016. Development of an integrated e-health tool for people with, or at high risk of, cardiovascular disease: The Consumer Navigation of Electronic Cardiovascular Tools (CONNECT) web application. International Journal of Medical Informatics. 134. O’Neill, K., 2013. Monitoring service delivery for universal health coverage: the Service Availability and Readiness Assessment. Bulletin of the World Health Organization, 91(12), pp.923–31. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3845262&tool=pmcentrez&rendertype=abstract. 135. Omaswa, D.C., 2013. ehealth Policy and elearning. Presented at the First Global Forum on Human Resources for Health. 136. Omidoyin, E.O., Opeke, R.O. and Osagbemi, G.K., 2016. Utilization Pattern and Privacy Issues in the use of Health Records for Research Practice by Doctors: Selected Nigerian Teaching Hospitals as Case Study. International Journal of Privacy and Health Information Management (IJPHIM), 4(1), pp.1–11. 137. Ong, M.H.A. and Fadilah Puteh, 2017. Quantitative data analysis: choosing between SPSS, PLS and AMOS in social science research. International Interdisciplinary Journal of 138. Scientific Research, 3(1), pp.14–25. Available at: https://www.tandfonline.com/doi/full/10.2753/MTP1069-6679190202. 139. Or, C.K.L.L., 2011. Factors affecting home care patients’ acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, 18(1), pp.51–59. 140. Ozok, A.A., 2014. Usability and perceived usefulness of personal health records for preventive health care : A case study focusing on patients ’ and primary care providers ’ perspectives. Applied Ergonomics, 45(3), pp.613–628. Available at: http://dx.doi.org/10.1016/j.apergo.2013.09.005. 141. Ozok, A.A., Wu, H. and Gurses, A.P., 2017. Exploring Patients’ Use Intention of Personal Health Record Systems: Implications for Design. International Journal of Human-Computer Interaction. 142. Palabindala, V., Pamarthy, A. and Jonnalagadda, N.R., 2016. Adoption of electronic health records and barriers. Journal of Community Hospital Internal Medicine Perspectives, 6(5), p.32643. Available at: https://www.tandfonline.com/doi/full/10.3402/jchimp.v6.32643. 143. Pappot, N., Taarnhøj, G.A. and Pappot, H., 2020. Telemedicine and e-Health Solutions for COVID-19: Patients’ Perspective. Telemedicine and e-Health. 144. Parlak, I.B. and Tolga, A.C., 2018. Health informatics. In: International Series in Operations Research and Management Science. 145. Patel, V.N., 2011. Consumer attitudes toward personal health records in a Beacon community. American Journal of Managed Care, 17(4). 146. Pearson, J.F., Brownstein, C.A. and Brownstein, J.S., 2011. Potential for electronic health records and online social networking to redefine medical research. Clinical Chemistry, 57(2), pp.196–204. 147. Perera, C., 2012. The Evolution of E-Health – Mobile Technology and mHealth. Journal of Mobile Technology in Medicine, 1(1), pp.1–2. Available at: http://www.journalmtm.com/2012/the-evolution-of-e-health-mobile-technology-and-mhealth/. 148. Phillippine PHR, 2014. Public Health Resources: Current Health Care Delivery System, 149. Poulymenopoulou, M., Malamateniou, F. and Vassilacopoulos, G., 2014. A virtual PHR authorization system. 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2014. 2014 pp. 73–76. 150. Pourhoseingholi, M.A., Vahedi, M. and Rahimzadeh, M., 2013. Sample size calculation in medical studies. Gastroenterology and Hepatology from Bed to Bench, 6(1), pp.14–17. 151. Price, M., 2015. Conditions potentially sensitive to a Personal Health Record (PHR) intervention, a systematic review. BMC Medical Informatics and Decision Making, 15(1), p.32. Available at: http://www.biomedcentral.com/1472-6947/15/32 [Accessed: 27 April 2015]. 152. R.J., B., B., S., J., D. and T.A., H., 2010. Information systems and healthcare XXXVII: When your employer provides your personal health record-exploring employee perceptions of an employer-sponsored PHR system. Communications of the Association for Information Systems, 27(1), pp.323–338. Available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-77958097813&partnerID=40&md5=715b651ba6f09e486ada870037da9bda. 153. Rahim, F.A. and Search, A.P., 2013. Information Privacy Concerns in Electronic Healthcare Records. 3rd International Conference on Research and Innovation in Information Systems, 2013, pp.504–509. 154. Rahimi, B., Nadri, H., Afshar, H.L. and Timpka, T., 2018. A systematic review of the technology acceptance model in health informatics. Applied Clinical Informatics. 155. Rasiah, R., Rosnah, N., Abdullah, W. and Tumin, M., 2011. Markets and Healthcare Services in Malaysia: Critical Issues. International Journal of Institutions and Economies, 3(3), pp.467–486. 156. Razmak, J. and Bélanger, C., 2018. Using the technology acceptance model to predict patient attitude toward personal health records in regional communities. Information Technology and People, 31(2), pp.306–326. 157. Reeve, J. and Hosking, R., 2013. Personal electronic health records : the start of a journey. , 36(3), pp.70–73. 158. Richards, R.J., 2013. A study of the intent to fully utilize electronic personal health records in the context of privacy and trust. Dissertation Abstracts International Section A: Humanities and Social Sciences, 74(4-A(E)), p.No-Specified. Available at: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:3533601%5Cnhttp://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=psyc10&NEWS=N&AN=2013-99190-583. 159. Ridder, H.G., Miles, M.B., Michael Huberman, A. and Saldaña, J., 2014. Qualitative data analysis. A methods sourcebook. Zeitschrift fur Personalforschung. 160. Ro, H.J., 2015. Establishing a Personal Health Record System in an Academic Hospital : One Year ’ s Experience. , pp.121–127. 161. Roehrs, A., Da Costa, C.A., Da Rosa Righi, R. and De Oliveira, K.S.F., 2017. Personal health records: A systematic literature review. Journal of Medical Internet Research. 162. Rosli, K., Yeow, P.H.. and Siew, E.-G., 2012. Computer-Assisted Auditing Tools Acceptance Using I-Toe : A New Paradigm. PACIS 2012 Proceedings. 163. Russell, S., 2018. Qualitative systematic review of barriers and facilitators to self-management of chronic obstructive pulmonary disease: views of patients and healthcare professionals. NPJ primary care respiratory medicine, 28(1), p.2. Available at: http://dx.doi.org/10.1038/s41533-017-0069-z. 164. Ryabko, D., 2019. Hypothesis Testing. In: SpringerBriefs in Computer Science. 165. S Sarifah Radiah, S., 2012. Chapter 3 Health Delivery System in Malaysia. 166. Samavi, R., Consens, M.P. and Chignell, M., 2014. PHR User Privacy Concerns and Behaviours. Procedia Computer Science, 37, pp.517–524. Available at: http://www.sciencedirect.com/science/article/pii/S1877050914010424$%5C$nhttp://www.sciencedirect.com/science/article/pii/S1877050914010424/pdf?md5=5dd6e868138364ee93ac9545f8b0efa5%7B&%7Dpid=1-s2.0-S1877050914010424-main.pdf. 167. Segall, N., 2011. Usability evaluation of a personal health record. AMIA ... Annual Symposium proceedings, 2011, pp.1233–42. Available at: http://www.ncbi.nlm.nih.gov/pubmed/22195184%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3243224. 168. Sekaran, U. and Bougie, R., 2016. Research methods for business: A skill building approach 7th ed., John Wiley & Sons, United Kingdom. 169. Sheikh, A., 2014. Adoption of electronic health records in UK hospitals: Lessons from the USA. The Lancet, 384(9937), pp.8–9. 170. Sittig, D.F. and Singh, H., 2012. Electronic Health Records and National Patient-Safety Goals. New England Journal of Medicine, 367(19), pp.1854–1860. Available at: http://www.nejm.org/doi/abs/10.1056/NEJMsb1205420. 171. Smith, M.I., 2017. Lessons learned after redesigning a personal health record. Studies in Health Technology and Informatics. 2017 172. Solachidis, V., 2018. Two examples of online eHealth platforms for supporting people living with cognitive impairments and their caregivers. ACM International Conference Proceeding Series. 2018 173. Srinivas, R. and Kumar, A., 2015. Attribute-Based Encryption for Reliable and Secure Sharing of PHR in Cloud Computing. International Journal of Computer Engineering in Research Trends, 125(10), pp.2349–7084. Available at: http://www.ijcert.org. 174. Sriwindono, H. and Yahya, S., 2013. Toward Modeling the Effects of Cultural Dimension on ICT Acceptance in Indonesia. Procedia - Social and Behavioral Sciences. 175. Suter, W., 2014. Sampling in Research. In: Introduction to Educational Research: A Critical Thinking Approach. 176. Tabibi, J., 2011. Effective factors on hospital information system acceptance: A confirmatory study in Iranian hospitals. Middle-East Journal of Scientific Research, 9(1), pp.95–101. 177. Taha, J., Czaja, S.J., Sharit, J. and Morrow, D.G., 2013. Factors affecting usage of a personal health record (PHR) to manage health. Psychology and Aging. 178. Taherdoost H, 2017. Determining Sample Size; How to Calculate Survey Sample Size. International Journal of Economics and Management Systems, 2(February 2017), pp.237–239. 179. Tarsi, K. and Tuff, T., 2012. Introduction to population demographics. Nat Educ Knowl, 3, p.3. 180. Tavares, J. and Oliveira, T., 2016. Electronic health record patient portal adoption by health care consumers: An acceptance model and survey. Journal of Medical Internet Research, 18(3). 181. Van Der Vaart, R. et al., 2014. Impact of patient-accessible electronic medical records in rheumatology: Use, satisfaction and effects on empowerment among patients. BMC Musculoskeletal Disorders. 182. Vance, B., Brent Tomblin, Jena Studney and Coustasse, A., 2015. Benefits and Barriers for Adoption of Personal Health Records. 183. Vance, B., Tomblin, B., Studeny, J. and Coustasse, A., 2014. Personal Health Records: Benefits And Barriers For Its Adoption.are tools which enable consumers to store their own health care information in one central location. The PHR may be as simple as an envelope containing a medication list, general health infor. Insights to a Changing World Journal, 2014(4), pp.48–67. Available at:http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=102471080&site=ehost-live. 184. Vasileios and Anastasios A., 2011. The acceptance and use of computer based assessment. Computers and Education. 185. Venkatesh, Viswanath;Thong, James Y.L.; Xu, X., 2012. Consumer Acceptance and Use of Information Technology. MIS Quarterly, 36(1), pp.157–178. 186. Venkatesh, Thong and Xu, 2018. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 187. Venkatesh, V., 2012, Technology Acceptance UTAUT [Online]. Available at: http://www.vvenkatesh.com/it/organizations/Theoretical_Models.asp#Con=structdefs. 188. Venkatesh, V., Thong, J. and Xu, X., 2018. Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems. 189. Vest, J.R., Yoon, J. and Bossak, B.H., 2013. Changes to the electronic health records market in light of health information technology certification and meaningful use. Journal of the American Medical Informatics Association, 20(2), pp.227–232. Available at: https://academic.oup.com/jamia/article-lookup/doi/10.1136/amiajnl-2011-000769. 190. Vimala, G. and Omar, S.Z., 2016. Implementation on ICT Knowledge Development in Healthcare. Available at: http://psasir.upm.edu.my/50757/. 191. Vogt, W., 2015. Research Hypothesis. In: Dictionary of Statistics & Methodology. 192. Wald, J.S. and Shapiro, M., 2013. Personalized health care and health information technology policy: An exploratory analysis. Studies in Health Technology and Informatics. 2013 pp. 622–626. 193. Wani, T.A. and Ali, S.W., 2015. Innovation Diffusion Theory: Review & Scope in the Study of Adoption of Smartphones in India. Journal of General Management Research. 194. Ward, R., 2013. The application of technology acceptance and diffusion of innovation models in healthcare informatics. Health Policy and Technology. 195. WHO, 2016. WHO Recommendation on Antenatal care for positive pregnancy experience. WHO Recommendation on Antenatal care for positive pregnancy experience. 196. WHO, 2010, Health - United Nations Sustainable Development [Online]. 197. Williams, M.D., Rana, N.P. and Dwivedi, Y.K., 2015. The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management. 198. Williamson, R.S., 2017. Meaningful Use of an Electronic Personal Health Record (ePHR) among Pediatric Cancer Survivors. Applied clinical informatics, 8(1), pp.250–264. Available at: http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=prem&NEWS=N&AN=28293684. 199. Witry, M.J., 2010. Family physician perceptions of personal health records. Perspectives in health information management / AHIMA, American Health Information Management Association, 7, p.1d. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2805556&tool=pmcentrez&rendertype=abstract. 200. Woods, S.S., 2013. Patient experiences with full electronic access to health records and clinical notes through the my healthevet personal health record pilot: Qualitative study. Journal of Medical Internet Research, 15(3). 201. Woosley, J.M., 2011. Comparison of Contemporary Technology Acceptance Models and Evaluation of the Best Fit for Health Industry Organizations . International Journal of Computer Science Engineering and Technology. 202. Wu, H., 2013. Exploring healthcare consumer acceptance of personal health information management technology through personal health record systems. 203. Xhafa, F., 2014. An efficient PHR service system supporting fuzzy keyword search and fine-grained access control. Soft Computing, 18(9), pp.1795–1802. 204. Yaacob, N.M., 2017. No Title. Journal of Advanced Research in Dynamical and Control Systems. 205. Yang, H., Lee, H. and Zo, H., 2017. User acceptance of smart home services: An extension of the theory of planned behavior. Industrial Management and Data Systems. 206. Yau, G.L., Williams, A.S. and Brown, J.B., 2011. Family physicians’ perspectives on personal health records: qualitative study. Canadian family physician Médecin de famille canadien, 57(5), pp.e178-84. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21642732%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3093606. 207. Yazdi-Feyzabadi, V., Emami, M. and Mehrolhassani, M.H., 2015. Health Information System in Primary Health Care: The Challenges and Barriers from Local Providers’ Perspective of an Area in Iran. International journal of preventive medicine, 6, p.57. 208. Yusif, S., Hafeez-Baig, A. and Soar, J., 2017. e-Health readiness assessment factors and measuring tools: A systematic review. International Journal of Medical Informatics, 107(June), pp.56–64. 209. Zhang, N.J.., 2013. Health information technology adoption in U.S. acute care hospitals. Journal of Medical Systems, 37(2). 210. Zhang, X., Yu, P., Yan, J. and Ton, S.I., 2015. Using diffusion of innovation theory to understand the factors impacting patient acceptance and use of consumer e-health innovations: A case study in a primary care clinic. BMC Health Serv Res, 15, p.71. Available at: http://www.ncbi.nlm.nih.gov/pubmed/25885110. 211. Zhivan, N.A. and Diana, M.L., 2012. U.S. hospital efficiency and adoption of health information technology. Health Care Management Science, 15(1), pp.37–47.