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Consumer Perceptions of the Adoption of Electronic Personal
Health Records: An Empirical Investigation
Collaborative Research ForumMarch 30, 2012
Mihail Cocosila Norm Archer
Athabasca University McMaster University
Faculty of Business DeGroote School of Business
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Agenda
o Introductiono Research motivationo Research objectiveso Theoretical modelo Methodologyo Key findingso Discussiono Limitationso Future research
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Introduction
• Unprecedented pressures in today’s society: provide better but also cost-effective care to an aging population affected by multiple health problems.
• One possible approach: encourage the support of healthcare Information Technology (IT) tools to provide information efficiently at the point of care.
• The most popular of these IT tools: Electronic Medical Record (EMR) or Electronic Health Record (EHR) systems - computer-based clinical records for record keeping and data storage in healthcare institutions and physician offices.
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… Introduction
• Personal Health Records (PHRs) tend to mirror EMRs or EHRs except that these newer electronic records are maintained by people monitoring their own health.
• Encouraging the use of PHRs may have positive consequences for all major stakeholders in the healthcare system: the general population receives better care while healthcare systems and society save money.
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Research Motivation
• User perceptions and views - key elements for the success of any IT deployment.
• Important for sensitive fields like e-Health.
• A limited review of the topic “electronic personal health record(s)” and “user perceptions” found relatively few papers on this subject.
• Knowledge gap: Lack of a scientific understanding of the perceptions of the Canadian population about the potential for adoption of PHRs.
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Research Objectives
• Important question: What are the key factors that influence intention to adopt PHRs?
• A well-documented body of research in information systems (IS): investigating user reasons to adopt a new IT.
• Various models and theories have been proposed and tested, depending on the IT artifact and context.
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… Research Objectives
• Information-Related factors favouring PHRs:o personal health knowledge; o satisfaction with their current medical care; o access to health data sources of interest;o health information-seeking preferences.
• Technology-Related factors favouring PHRs:o Internet reliance; o computer self-efficacy; o personal IT innovativeness; o security, privacy, and trust in PHR systems;o computer anxiety.
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… Research Objectives
• Research question:
o What are the key information-related and technology-related factors that influence consumer perceptions of the adoption of electronic Personal Health Record systems?
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Theoretical Model
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Methodology
• Cross-sectional study with 400 participants in Canada recruited through a commercial firm.
• Including conditions:o be at least 18 years old;o not claiming to have a chronic illness.
• A total of 383 valid cases after removing incomplete ones.
• Data analysis: descriptive statistics, Partial Least Squares (PLS) modeling.
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Key FindingsDemographics:
Characteristic Findings
Age (average) 46.5Gender 57.7% Female, 42.3% MaleMaintain up-to-date PHRs on paper 19.2%Yes, 80.8% NoMaintain up-to-date electronic PHRs 5.8%Yes, 94.2% NoNumber of visits with a doctor during past 6 months (average)
2.3
Number of doctors seen during past 6 months (average) 1.4Number of children 12 years old or younger for whom they have main care responsibility at home (average)
0.3
The subject (or someone for whom they are responsible) has a chronic disease that requires continuing medical attention
21.2%Yes, 78.8% No
The subject (or someone for whom they are responsible) has a disability that requires continuing care
11.0%Yes, 89.0% No
Caring for elderly person(s) 5.5%Yes, 94.5 % NoInterested in regularly maintaining records about health 64.4%Yes, 35.6% NoAverage amount of time spent using the Internet at home daily (largest two categories)
Between 31 and 60 minutes 58.0%, Between 11 and 30
minutes 29.0%
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… Key Findings
Significance levels:
* = 0.05;
** = 0.01;
*** = 0.001
Assessment of
Structural model
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… Key Findings
Total Effects on Behavioural Intention
Coefficient p-Value
Information Factors
Personal Health Knowledge 0.023 0.198
Satisfaction with Medical Care -0.007 0.627
Access to Data Sources 0.043 0.203
Information Seeking 0.111 0.154
Technology Factors
Internet Reliance 0.028 0.206
Computer Self-Efficacy 0.095 0.112
Personal IT Innovativeness 0.165 0.014
Security, Privacy and Trust 0.487 0.000
Anxiety -0.143 0.046
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Discussion
Answers to the research question (key information-related and technology-related factors that influence consumer perceptions of the adoption of electronic Personal Health Record systems)o Similar to previous research, Perceived Usefulness is the single
most important antecedent of the intention to use PHRs (path coefficient 0.570, significant at a 0.001 level);
o From an information point of view, people would use PHRs only because they see the usefulness of these devices when seeking health information;
o Most of the technology factors have a significant positive role in the adoption equation - computer self-efficacy is the only exception;
o As expected, computer anxiety has a negative significant influence.
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… Discussion
Answers to the research question
• Technology factors are more important than information factors in the adoption equation.
• As expected for a sensitive field, Security, Privacy and Trust is of outmost importance followed by Personal IT Innovativeness and Anxiety.
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… Discussion
Answers to the research question
• Possible explanation of the findings: participants were more consumers than patients (78.8% did not report having a chronic disease, 89% and 94.5% did not care for a person with disabilities or an elderly person, respectively).
• Participants were unlikely to be concerned about seeking medical information but seemed to be fairly experienced with the Internet and, thus, with computers and IT.
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Limitations
• Sample convenience: from a panel of participants pre-recruited by a surveying firm.
• Actual medical condition of the participants was unknown since this was self-reported (e.g., having a chronic illness or not).
• Overall, not more limitations than those usually reported in relevant IS literature.
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Future Research
• Look in more detail at possible other factors of adoption (e.g., risk perceptions).
• Contrast with other samples, including patients needing regular medical care.
• Experiment with exposing participants to PHR applications.
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Message to Remember
Before encouraging large scale deployments, it is critical to better understand consumer views of PHR
systems and their potential for supporting better care, possibly at lower cost to society.
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• Paper submitted at the Eighteenth Americas Conference on Information Systems, Seattle, Washington, August 9-12, 2012.
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Thank you!
Questions?
Mihail Cocosila, PhDAthabasca University, Faculty of Business
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