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TitleA Study of the Intent to Fully Utilize Electronic Personal Health
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LanguageEnglish
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APPROVED:

John Windsor, Major Professor
Steve Guynes, Committee Member
Mark Davis, Committee Member
Audhesh Paswan, Committee Member
Mary C. Jones, Chair of the Department of

Information Technology & Decision
Science

O. Finley Graves, Dean of the College of
Business

James D. Meernik, Acting Dean of the
Toulouse Graduate School

A STUDY OF THE INTENT TO FULLY UTILIZE ELECTRONIC PERSONAL HEALTH

RECORDS IN THE CONTEXT OF PRIVACY AND TRUST

Rhonda J. Richards, B.S., M.B.A.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY




UNIVERSITY OF NORTH TEXAS


May 2012

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Richards, Rhonda J., A Study of the Intent to Fully Utilize Electronic Personal Health

Records in the Context of Privacy and Trust. Doctor of Philosophy (Business Computer

Information Systems), May 2012, 222 pp., 29 tables, 6 figures, bibliography, 205 titles.

Government initiatives called for electronic health records for each individual healthcare

consumer by 2014. The purpose of the initiatives is to provide for the common exchange of

clinical information between healthcare consumers, healthcare providers, third-party payers and

public healthcare officials. This exchange of healthcare information will impact the healthcare

industry and enable more effective and efficient application of healthcare so that there may be a

decrease in medical errors, increase in access to quality of care tools, and enhancement of

decision making abilities by healthcare consumers, healthcare providers and government health

agencies. An electronic personal health record (ePHR) created, managed and accessed by

healthcare consumers may be the answer to fulfilling the national initiative. However, since

healthcare consumers potentially are in control of their own ePHR, the healthcare consumer

concern for privacy may be a barrier for the effective implementation of a nationwide network of

ePHR.

A technology acceptance model, an information boundary theory model and a trust model

were integrated to analyze usage intentions of healthcare consumers of ePHR. Results indicate

that healthcare consumers feel there is a perceived usefulness of ePHR; however they may not

see ePHR as easy to use. Results also indicate that the perceived usefulness of utilizing ePHR

does not overcome the low perceived ease of use to the extent that healthcare consumers intend

to utilize ePHR. In addition, healthcare consumers may not understand the different components

of usage: access, management, sharing and facilitating third-party ePHR. Also, demographics,

computer self-efficacy, personal innovativeness, healthcare need and healthcare literacy impact a

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other incremental fix indices such as comparative fit index (CFI) were used to assess the

fit of the structural model.

A sample size of 500 healthcare consumers to be surveyed was planned which

would provide usable results for this study. There are 21 measurable constructs or free

parameters estimated in this model. The common rule of 10 for SEM modeling is 10:1 for

participants to latent variables which would indicate a sample size of 210 (Schreiber,

Nora, Stage, Barlow, & King, 2006). While some studies in the field of management

information systems (MIS) have shown that there may be better methods than the rule of

ten, due to the size and nature of this study, those methods are not possible within the

length and budget of this study (Westland, 2010).

in SEM research which is understood to provide sufficient statistical

power for data analysis (Garver & Mentzer, 1999) However, due to the recent concern

regarding the sample size of MIS studies utilizing SEM, every effort was made to collect

a sufficient number of usable surveys (Westland, 2010). Plans were in place if sufficient

data was not collected initially at the UCCs, alternative methods of collection would

begin. Alternative methods included seeking participation from students, organizations

and/or Facebook requests and after lower than expected involvement from clinic visitors,

were implemented into the study.

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Westland, J. C. (2010). Lower Bounds on Sample Size in Structural Equation Modeling.

Electronic Commerce Research and Applications, 476-487.

Wild, K. R. (2010). The Evolution of HIPAA: The Only Constant is Change. Journal of

Health Care Compliance, 33-36.

Willison, D., Schwartz, L., Abelson, J., Charles, C., Swinton, M., Northrup, D., et al.

(2007). Alternatives to project-specific consent for access to personal information

for health research. What is the opinion of the Canadian public? Journal of

American Medical Informatics Association, 527-533.

Wilson, E. V., & Lankton, N. (2004). Modeling Patients' Acceptance of Provider-

Delivered E-health. Journal of American Medical Informatics Association, 241-

248.

Withrow, S. C. (2010). How to Avoid a HIPAA Horror Story. Healthcare Financial

Management, 82-88.

Xu, H. (2007). The Effects of Self-Construal and Perceived Control on Privacy Concerns.

Proceedings of the 28th Annual International Conference on Information Systems.

Montreal: ICIS.

Xu, H., Smith, J., Dinev, T., & Hart, P. (2008). Examining the Formation of Individual's

Privacy Concerns: Toward an Integrative View. International Conference on

Information Systems (pp. 1-16). Paris: Association for Information Systems.

Yasnoff, W. A. (2008, December 22). Electronic Records are Key to Health-Care

Reform. BusinessWeek Online, p. 27.

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