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Increasingly popular in the health care domain, electronic personal health records (PHRs) have the potential to foster engagement toward improving health outcomes, achieving efficiencies in care, and reducing costs. Despite the touted benefits of PHRs, their uptake is lackluster, with low adoption rates.
This paper reports findings from an empirical investigation of the sociotechnical factors affecting the adoption of PHRs.
A research model comprising personal and technological determinants of PHR adoption was developed and validated in this study. Demographic, technographic, and psychographic data pertaining to the use of PHRs were collected through a web-based questionnaire for past, current, and potential users. Partial least squares-based structural equation modeling was used to estimate a structural model of cognitive and affective factors impacting intentions to use PHRs.
The analysis revealed that in addition to the expected positive impact of a PHR system’s usefulness and usability, system integration also positively affects consumers’ intention to adopt. The results also suggest that higher levels of perceived usability and integration do not translate into higher levels of perceived usefulness. The study also highlights the importance of subjective norms, technology awareness, and technology anxiety as direct antecedents of the intention to adopt PHRs. The differential effects of the adoption factors are also discussed.
We hope that our study will contribute to the understanding of consumer adoption of PHRs and help improve the design and delivery of consumer-centric health care technologies. After discussing the implications for research, we provide suggestions and guidelines for PHR technology developers and constituents in the health care delivery chain.
Within the realm of health systems and applications, electronic personal health records (PHRs) represent a burgeoning technology that is gaining traction in many countries worldwide [
In this paper, we characterize PHR technologies as those specifically pertaining to digitally stored health care information about an individual patient under the control of that patient or their caregiver [
Industry analysts have predicted great market potential for PHR-related technologies. For instance, according to studies conducted by the Markle Foundation, over 70% of US health care consumers believe that PHRs can improve health care quality [
At the macro level, leveraging the potential value of PHRs in facilitating patient engagement and improving consumer health outcomes has been a key constituent of several government eHealth initiatives around the world. For example, in the United States, the Health Information Technology for Economic and Clinical Health Act established a meaningful use incentive program offering financial support to providers and health systems adopting EHR-related technologies [
Notwithstanding the industry forecasts about abundant consumer interest and government commitments to PHR technologies, the adoption of these technologies has been much slower than originally expected [
In delineating the scope of investigation of this study, we would like to highlight our deliberate use of the term consumer instead of patient throughout the discussion. Our objective is to investigate factors that impact the adoption of PHRs from the perspective of all users who may be current as well as potential users of these systems. Toward this, we aim to include not only users who are currently receiving active care (patients), but also those who may simply be interested in maintaining their health information and medical history, or in using other nonclinical functionalities of PHRs (consumers). Other academic researchers and industry analysts have also commented on the distinction between patients and consumers, noting that consumers may include both current and prospective patients [
By virtue of its orientation, this research study is principally situated in the field of consumer health informatics (CHI), a field concerned with health and health care-related preferences and information needs of consumers and associated medical and public health practitioners [
Researchers who have investigated user adoption of PHRs have suggested that possible adoption barriers may be related to technology factors, such as privacy and security concerns, system usability, and poor integration with health care provider systems [
Consequently, researchers have called for further empirical studies to explore and validate the role of specific PHR adoption factors.
Our review of the extant literature indicates that patients with chronic illnesses or disabilities, their caregivers, and people caring for older persons are more likely to adopt and use PHR technologies [
Current research also shows that factors such as computer anxiety, security and privacy concerns, and perceptions of usefulness are key determinants of PHR adoption across different consumer strata [
In terms of key areas for further exploration, our review indicates the need for more research on PHR adoption along several lines. From the perspective of personal factors, there is a significant lack of empirical evidence on the role of social influence processes in PHR adoption. In our review, we found only two studies that investigated the role of subjective norms in the adoption of PHRs [
Notwithstanding the differences in results across some studies, researchers continue to investigate factors impacting consumer adoption of PHRs with the aim of improving our cumulative understanding of this phenomenon. As such, additional research in this area has been recommended by many researchers to further explore the impact of personal, technological, organizational, and environmental factors on consumer acceptance of PHR technologies, including patients and their caregivers [
This paper answers the call by theorizing and validating the role of various personal and technological factors as possible determinants of PHR adoption. We aim to contribute to the body of knowledge on the adoption of PHR systems by exploring sociotechnical factors that not only further clarify or complement those previously studied by other researchers, but also offer new avenues of inquiry. The scope of our investigation includes the study of subjective norms, technology awareness, and technology anxiety as personal factors affecting PHR adoption, and system integration, perceived usefulness, and perceived usability as technological antecedents of PHR adoption. These constructs and their definitions are provided in
Research model constructs.
Theme and constructs | Conceptual definition | |
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Subjective norm |
The degree to which users perceive that most people who are important to them think they should or should not use the system [ |
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Technology awareness |
An individual’s familiarity with the purpose and benefits of the technology [ |
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Technology anxiety |
An individual’s apprehension or fear when confronted with the use of technology [ |
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System integration |
Extent of connection and interoperability among technology components and subsystems [ |
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Perceived usefulness |
The degree to which users believes that using the system will help them toward achieving their desired goals [ |
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Perceived usability (ease of use and accessibility) |
The degree of ease associated with the system [ Intuitive interface and information structure that is comprehensible and available when needed [ |
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Behavioral intention |
The degree to which a person has formulated conscious plans to perform or not perform some specified future behavior [ |
Research model and construct definitions.
In technology adoption studies, the concept of subjective norm is appropriated to account for social influences that impact a potential user’s decision to adopt and use a technology. The concept of subjective norm has its theoretical underpinnings in the theory of reasoned action, which defines it as “person’s perception that most people who are important to him think he should or should not perform the behavior in question” [
In the context of PHR system adoption, there is a dearth of research exploring the role of social influence on a user’s decision to adopt these technologies. In our literature review, we identified one study that investigated subjective norms in the context of hardware-based (USB) PHRs within the specific regional context of Taiwan [
H1: Favorable subjective norms pertaining to the use of PHR technologies have a positive effect on the behavioral intention to use PHRs.
H2: Favorable subjective norms pertaining to the use of PHR technologies have a positive effect on the perceived usefulness of PHRs.
Despite PHR technologies having been introduced more than a decade ago, research has found that there is a lack of awareness about them among many potential end users [
In addition to exploring the role of technology awareness as a direct antecedent of perceived usefulness, we also explored its relationship with subjective norms. Research literature on the diffusion of innovation considers interpersonal relationships as an effective channel for creating awareness about an innovation [
H3: Favorable subjective norms pertaining to the use of PHR technologies have a positive effect on technology awareness of PHRs.
H4: Greater technology awareness of PHR technologies has a positive effect on the perceived usefulness of PHRs.
Previous research in ISs shows technology anxiety to be a significant barrier to the adoption of new technologies [
To address this gap in PHR adoption research, our model posits technology anxiety as an affective construct that affects the adoption of PHRs. We explored the direct link between anxiety and behavioral intention and its indirect effect on perceptions of usability (ease of use and accessibility). In doing so, our model attempts to capture the varying causes and effects of anxiety expressed in the extant literature on PHR adoption. These include inadequate technology literacy [
H5: A higher level of technology anxiety has a negative effect on the perceived usability of PHRs.
H6: A higher level of technology anxiety has a negative effect on the behavioral intention to use PHRs.
Among the various contemporary PHR architectures, one may expect greater consumer interest in interconnected PHRs rather than stand-alone PHRs or even tethered PHRs. It is our position that with greater access to health and medical information available through multiple sources, consumers may be more motivated to use PHR systems. Such systems are likely to garner more interest through their
Although many researchers and industry experts have commented on the lack of interoperability as a major barrier to consumer adoption [
H7: Greater system integration in PHR technologies has a positive impact on the perceived usefulness of PHRs.
H8: Greater system integration in PHR technologies has a positive impact on the behavioral intention to use PHRs.
The extensive body of knowledge on the technology acceptance model (TAM) [
H9: The higher perceived usefulness of PHR technologies has a positive impact on the behavioral intention to use PHRs.
Our final technological construct in the research model is theorized as a multidimensional factor consisting of the dimensions of perceived ease of use and perceived accessibility. The perceived usability construct in our model aims to capture the notion of effort expectancy associated with PHR systems, that is, the degree of ease associated with using PHRs.
The traditional view of the perceived ease of use construct in TAM also signifies effort expectancy [
In conceptualizing perceived usability, we retain
H10: Greater perceived usability of PHR technologies has a positive impact on the perceived usefulness of PHRs.
H11: Greater perceived usability of PHR technologies has a positive impact on the behavioral intention to use PHRs.
To characterize the adoption of PHRs, we used behavioral intention as the ultimate downstream construct in our research model. As a critical outcome of various cognitive and affective antecedents, this construct has its original basis within the theory of reasoned action [
Overall, our research model aims to offer an inclusive basis for validating the role of three different types of determinants on PHR adoption—(1) individual differences, (2) system characteristics, and (3) social influence. Research models that include these categories of factors have been recommended as a practical foundation for investigating the adoption of new technologies [
In terms of organization, our empirical methodology is described in terms of key procedures, and the results of our investigation are outlined. Finally, the discussion and conclusion sections offer an interpretation of the results, especially with respect to their implications for research and practice.
The research model posited in the previous section was validated through a quantitative empirical investigation using a web-based survey instrument. Details of the survey content, measurement scales, analysis procedures, and data collection techniques are presented below.
The survey comprised
To develop the two new scales, various qualitative and quantitative content validity assessment procedures were used, including concept elicitation interviews with subject matter experts (n=7) to generate representative and relevant measurement items; cognitive interviews with potential respondents from the target sampling frame (n=5) to ensure item relevance and clarity, and the final selection of measurement indicators based on item relevance ratings of subject matter experts, which were subsequently used to calculate item-level content validity indices (I-CVI). Drawing upon recommendations from the extant literature [
At the end of the survey, participants were also invited to optionally respond to this open-ended question about PHR use: “Do you have any other comments about the use of personal health records (PHRs)? What factors do you consider to be important in your decision to start using or keep using technologies such as PHRs?”
The complete survey instrument was assessed for face validity through consultations with other HIS researchers, and construct validity for each theoretical construct was assessed through exploratory factor analysis of the pilot survey responses (n=20).
Data for this study were collected through a web-based survey administered to actual and potential users of PHR technologies. Screening questions were asked at the beginning of the survey to determine different classes of respondents, and a brief overview of PHR technologies was offered to ensure qualified responses. As outlined in
The sampling techniques used were primarily based on convenience and self-selection. We recruited respondents who had basic familiarity with PHRs or similar tools for health care self-management. We used a two-pronged approach for data collection to ensure a cross section of potential PHR consumers. First, we solicited participation from current and past users of a PHR portal sponsored and supported by a teaching hospital (tethered PHR) in Ontario, Canada. In distributing our call for participation, we emphasized our interest in obtaining responses from current and past users of the PHR system. Second, calls for participation were also communicated through various web-based forums and social media groups dedicated to the discussion of health-related topics. To ensure a diverse selection of respondents, our sampling frame included both general health and wellness sites, as well as sites for chronic illness support groups. Once again, we underlined our goal of including responses from existing and potential users of PHR technologies.
Permission was sought from site administrators or forum moderators before posting our call for participation. In the case of the hospital PHR, our call for participation was distributed by the administrator to a mailing list of PHR users who had opted to receive news and information from the website at the time of their registration with the portal. No respondent incentives were offered for completing the survey.
The survey responses were collected over a 4-week period, with one reminder posted at each site with the original call for participation. Key suggestions from the Dillman tailored design method [
Because partial least squares (PLS) was the planned multivariate statistical analysis procedure in this study, the minimum sample size heuristic for PLS studies [
Responses to demographic and technographic questions were analyzed using descriptive statistics and nonparametric statistical tests, and testing of research model constructs and hypotheses was conducted through exploratory factor analysis and PLS-based structural equation modeling (SEM) techniques. The PLS approach for SEM was selected for this study because of its suitability for small-sample exploratory research [
Testing for common method bias was achieved by using three different procedures—(1) the Harman post hoc one-factor test [
A total of 224 responses were collected from various sources, including the hospital PHR portal, web-based forums, and social media groups in our sampling frame. After discarding partial responses, 168 responses were retained for further statistical analysis. This exceeded our minimum sample size target, as specified above. The results from our analysis of the survey responses are detailed in the following subsections.
Key highlights from the respondent sample (n=168).
Demographic and technographic factors | Frequency, n (%) | ||
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Female | 96 (57.1) | |
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Male | 72 (42.9) | |
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18-25 | 22 (13.1) | |
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26-35 | 31 (18.5) | |
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36-45 | 66 (39.3) | |
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46-55 | 28 (16.7) | |
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55 or older | 21 (12.5) | |
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PHRa portal | 59 (35.1) | |
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Online health communities | 109 (64.9) | |
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Familiar | 116 (69.1) | |
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Current use | 64 (38.1) | |
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Past use | 30 (17.9) | |
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Patients | 66 (39.3) | |
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Caregivers | 39 (23.2) |
aPHR: personal health record.
On the survey question pertaining to the importance of various health care issues, respondents consistently identified better clinical health care outcomes as the top priority for them. These were followed by issues surrounding better delivery of health care, including access and cost of health care, as well as better communication with physicians.
The next section outlines the results of the assessment of psychographic variables in the posited research model. Following the two-step approach for SEM analysis suggested by Anderson et al [
The measurement model was assessed through a combination of exploratory factor analysis procedures and various tests for discriminant and convergent validities for the constructs in the research model.
We assessed our multidimensional operationalization of the
For our main measurement model, we inspected the loading and cross-loading of the indicators, as presented in
We also followed the Fornell and Larcker guidelines [
Various tests of convergent validity were performed through an assessment of quality indices, as shown in
Finally, as part of the measurement model, we assessed the possibility of the common method bias using three different procedures.
First, the Harman post hoc one-factor test [
We subsequently applied the procedure specified by [
Finally, we used the PLS-based common method bias test suggested by Liang et al [
Overall, the assessment of the measurement model was deemed satisfactory in terms of item reliability and discriminant validity, and the model constructs were considered to be internally consistent as a measurement scale.
Following the measurement model assessment, the structural model was estimated to provide details of the strengths of the relationships among the latent constructs and the overall predictability of the endogenous latent variables in the model.
To estimate the structural model, path coefficients and significance levels were obtained by running PLS with bootstrapping using 1000 resamples. The structural model and
Estimated structural model. *
With respect to
The results pertaining to
To determine the efficacy of the model in terms of predictability and goodness of fit (GoF), the coefficients of determination (R2) and average communality (AVE) for each construct were evaluated. Together, these measures were used to calculate the global criterion of GoF, as recommended by several researchers [
The R2 values suggest that the model performed well for the endogenous variables pertaining to technology awareness, perceived usefulness, and behavioral intention. These coefficients of determination (R2) explain the proportion of a construct’s variance that can be predicted by antecedent constructs in the model. Most endogenous variables in the model compellingly exceed the minimum threshold of 0.10, indicating the usefulness of that variable in the model [
To calculate the GoF index, the average communality of each construct is calculated as a weighted average of communality (AVE) based on the number of items in each construct taken as its weight [
On the basis of the evaluation of the measurement model validity and reliability, as well as the verification of predictive relevance and GoF of the structural model, we believe that the structural equation model was able to establish a strong basis for relationships posited in the research model hypotheses. Overall, the proposed model acts as an adequate predictor of behavioral intention to use PHRs.
As outlined earlier, we asked survey respondents to optionally provide comments about PHRs through textual responses to the question, “Do you have any other comments about the use of personal health records (PHRs)? What factors do you consider to be important in your decision to start using or keep using technologies such as PHRs?”
A total of 63 responses were submitted, and these were analyzed using simple content analysis techniques at the manifest level. In coding and classifying the qualitative data, we searched for themes or concepts related to the adoption of PHRs. An emergent coding technique was used whereby two researchers independently reviewed the responses and created a list of themes and codes. The list was consolidated after mutual consultation.
Content analysis summary for open-ended survey responses (n=63).
Themes and comments | Frequency, n (%) | ||
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40 (64) | ||
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Support the idea of PHRs looking forward to their wider availability | 16 (25) | |
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PHRs are useful as they provide control or options to patients and their families | 12 (19) | |
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PHRs useful for chronic illness patients | 14 (22) | |
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Willing to pay or subscribe for PHR technologies | 4 (6) | |
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16 (25) | ||
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Medical information patient and provider records | 8 (13) | |
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Contact and communication with physician or provider | 6 (10) | |
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Decision support tools | 4 (6) | |
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Shared access and social networks | 3 (5) | |
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18 (29) | ||
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Prefer data integration; unwilling to do manual data entry | 12 (19) | |
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Security and privacy concerns | 11 (18) | |
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Should be available through mobile apps | 6 (10) |
aPHR: personal health record
On the whole, many respondents commented on the usefulness of PHR technologies as a whole and indicated their support and anticipation in adopting these technologies. Features related to the maintenance of medical information and online communication with physicians emerged as the most commonly cited PHR functions of interest. Interoperability, security, and privacy issues were frequently mentioned as key factors in the PHR adoption decision. Finally, some respondents stated their interest in using PHR technologies through mobile apps, hence alluding to the notion of accessibility as an important consideration for them.
The results outlined in the previous section corroborate the general premise that a combination of personal and technological factors plays a role in determining the adoption of PHR technologies. In exploring these factors, our study has attempted to integrate constructs related to social influence beliefs (subjective norm), individual affective states (awareness and anxiety), and cognitive instrumental perceptions (system integration, perceived usability, and perceived usefulness) that potentially impact adoption behavior (behavioral intention) toward PHR technologies. This section provides an interpretation of the results and discusses the implications for research and practice.
Our results indicate that a person’s judgment of subjective norms pertaining to the use of PHR systems plays an important role in the adoption of these technologies through multiple cognitive and affective processes. Its direct impact on behavioral intention suggests that social influence plays a role in people’s decision to adopt PHR technologies. The relatively weak association between subjective norms and behavioral intention can also be explained with reference to past research that shows that subjective norm does not factor prominently as a direct antecedent of behavioral intention in situations where the use of technology is voluntary [
The positive impact of technology awareness on perceived usefulness also alludes to a process of internalization whereby consumers’ familiarity with the various use cases of PHR technologies allows them to develop beliefs about the technology’s overall usefulness to them. Because the use of PHR systems is voluntary, it is reasonable to assume that consumers would take time to discover and understand the technology before deciding to adopt it. Once again, the relationship between subjective norms and technology awareness implies that observations and interactions with other people play an important role in this process.
Our results also support the critical role of technology anxiety as a determinant of PHR system adoption. Although no significant relationship emerged between technology anxiety and perceived usability, the construct exhibited a significant direct impact on behavioral intention to adopt PHR technologies. With respect to the former, although recent IS studies have shown anxiety to be an important antecedent of perceived ease of use [
The construct of system integration was theorized in our research to measure the importance that users confer on interoperability (among PHRs and other back-end EHR or EMR systems) in their decisions to adopt PHR technologies. Our results demonstrate a positive association between consumer beliefs about PHR interoperability and the intention to adopt these technologies. However, the lack of support for the relationship between system integration features and perceptions of the usefulness of PHR technologies is counterintuitive. In the context of PHRs, it can be expected that better functionality of these systems in terms of connection and interoperability with other back-end systems would translate into better perceptions of the system’s usefulness. This posture is supported by current research on PHR systems that consider a lack of integration between patient-facing systems and back-end eHealth systems as a barrier to adoption for both consumers and health care professionals [
These differential effects of system integration beliefs can be explained in the context of user expectations. It may be the case that given today’s vast user experience with web-based tools and the pervasive deployment of web services linking different web-based systems, users simply expect PHR systems to be interoperable at the outset. Their common perception about PHRs would align with tethered and interconnected system models of PHRs, and it is these types of technologies that users are interested in adopting. Consumers may factor in these aspects of interoperability only during the initial stages of adoption, and these features are not internalized over time into higher-order cognitive states that represent perceptions of the usefulness of the system. As such, in our research model, the system integration construct is conceptualized in the form of initial expectations pertaining to PHR technologies, and it does not capture or measure aspects of assimilation of these technologies. Therefore, we suggest that future studies use a different approach to model the relationship between system integration and perceived usefulness. One possibility may be to draw upon the experience-disconfirmation theory, which has its roots in consumer behavior research [
Unlike many studies investigating technology adoption, our study did not find a significant relationship between perceived usability and perceived usefulness. Although this finding may be at odds with the general IS literature, the findings are not completely surprising in the specific context of PHR system adoption. Previous studies on PHR technology adoption have also shown varied results regarding the effects of perceived ease of use. Some studies confirm construct relationships as defined in the original TAM [
In terms of direct effects on behavioral intention to adopt PHR systems, our results are consistent with the extant research literature. The role of perceived usability and perceived usefulness as antecedents of behavioral intention to adopt PHR systems was validated. Furthermore, having demonstrated internal reliability and construct validity, our integrated conceptualization of perceived usability as a combination of perceived ease and accessibility shows promise in the context of studying PHR technologies. Conceptualization lends support to many researchers’ viewpoints on the synergistic relationship between usability and accessibility [
Responses to technographic questions and the open-ended questions in our survey also reveal consumer preferences for specific PHR features and functions. Our findings contribute to answering the call by other researchers, such as [
Future studies should further investigate the role of norm internalization and technology assimilation as individual psychological processes affecting behavior toward PHR technologies. We suggest that the relationships among sociotechnical constructs reflect a gradual process in the development of beliefs about PHR technologies and their consequent adoption. For example, in this study, our results suggest that subjective norm and technology awareness are key constructs that affect the consolidation of individual and social values into higher-order cognitive beliefs about the purpose of the benefits of PHR technologies, that is, the internalization process. In the same vein, technology attributes, such as system integration and usability, feature more prominently in the affective and cognitive processes pertaining to technology assimilation. As a possible avenue for future investigations, we believe that incorporating mediating constructs from experience-disconfirmation theory could provide potentially valuable insights into PHR adoption research.
Future research should also seek to explore and validate the potentially multidimensional nature of some of the personal constructs posited in our theoretical model. Specifically, technology anxiety should be studied in terms of anticipatory and situational anxiety. We believe that both of these dimensions play an important role during the different stages of adoption of PHR technologies. Similarly, on the technology side, system integration should be operationalized through specific attributes of integration, such as single window patient information access, system-to-system health data sharing, and information communication capabilities, such as patient-physician exchanges. Doing so would also have the added benefit of deconstructing the specific needs and preferences of consumers in terms of their expectations of integration features and functions between PHR technologies and other HISs.
Our research also provides opportunities to improve health technology assessments. The conceptualization of the two new technology factors of system integration and perceived usability offered in our study may help enhance future systematic evaluations of health care technology. As highlighted earlier, our research shows that functionality, ease of use, and accessibility all play an important role in the adoption of PHR technologies.
In terms of practical implications, our research offers recommendations for PHR technology developers and designers, solution vendors, clinicians, and health policy makers.
Our study highlights the importance of system integration as a significant element affecting the initial decision to adopt PHRs. Technology developers should aim to incorporate interoperability as much as possible. Given the various challenges that exist in achieving seamless point-to-point integration across various types of HISs, developers and vendors should consider the use of health information exchanges as a viable alternative. Industry research suggests that health information exchanges may provide a practical solution to ensuring consumer access to comprehensive longitudinal health records from across the health care delivery chain [
PHR technology designers should also strive to incorporate accessibility as an element of overall PHR usability. In addition to being easy-to-learn and efficient-to-use, PHR tools should be available through a variety of channels, such as desktop, web, and mobile. Furthermore, PHR systems should facilitate help options and learning pathways to assist end user interactions with the technology features of PHR systems and to support a gradual learning curve. Technology should be developed in such a way as to mitigate anticipatory and situational anxiety with PHR technologies, and it should help end users feel in control of the system. A delineation of basic versus advanced features, context-sensitive suggestions for tasks and actions, and readily available technical support may help alleviate user anxiety and support the adoption of PHR systems [
Technology vendors can also help improve the uptake of their PHR systems by influencing personal affective and cognitive beliefs that influence behavior toward PHR technologies. For example, technology awareness can be improved and technology anxiety can be reduced by incorporating additional aspects of trialability and observability in PHR offerings. The availability of free trial versions or free subscriptions, interactive demonstration vignettes and how-to-use videos, access to a community of end users, and spotlights on positive consumer stories can provide useful mechanisms to help alleviate challenges pertaining to technology anxiety and awareness.
Health care providers and practitioners can help improve the uptake of PHR technologies by integrating these tools into clinical encounters and by engaging patients with the technology along various touchpoints in care delivery. The long-term benefits expected from the effective use of these technologies could potentially outweigh any increase in the short-term workload experienced by practitioners in helping promote these technologies to their patients.
From a policy perspective, relevant government agencies can prioritize training and development initiatives for people to become more proficient with the use of PHR systems. The target audience for such programs could include both consumers and health care professionals. The latter factor into the technology adoption process as key influencers as their engagement with patients and their endorsement of relevant PHR applications can accelerate the uptake of these technologies. Government-sponsored technology demonstrations can be administered at community centers or libraries to help improve literacy about PHR technologies, thereby improving consumer awareness, reducing anticipatory anxiety, and leading to greater adoption of these systems. Finally, at the infrastructure level, governments can accelerate the development of interoperability and health data interchange standards that would help make these systems more attractive to consumers and enable faster mainstream adoption.
To further confirm the relevance of our research to the health care sector, we performed applicability checks with several health care professionals, including two physicians, one hospital administrator, one system developer, and one health policy analyst. Applicability checks have been recommended as a useful method for researchers to improve communication between research and practice [
Applicability checks and comments from health care professionals.
Health care professional | Perspective | Key comments |
General practitioner (family medicine) | PHRa adoption for improved clinical health outcomes |
“PHRs can be great tools to allow patients to become more informed about their conditions and treatments.” “I believe that we can help patients get familiar with the benefits of PHRs and also help them get over their initial hesitation in using these tools.” |
Primary care physician (pediatrics) | PHR adoption for improved clinical health outcomes |
“I think PHR tools can be great for parents to keep track of their children’s medical history. The information can later be handed over to children once they are able to manage it themselves.” “Once the technical hurdles are resolved, I think clinicians can play an important role in encouraging people to use these technologies. However, we [physicians] have to start using them too and lead by example.” |
Hospital administrator (director of operations) | PHR adoption for ensuring continuity of care |
“We currently provide access to patients to a limited part of their medical records. Having an integrated medical record across healthcare organizations can be very useful for timely interventions.” “As pointed out in this research, there are many technical obstacles to providing an integrated medical record and this probably hurts overall adoption.” |
Systems developer (EHRb systems; mobile health apps) | Functionality and usability requirements for PHR adoption |
“Providing access to patient information across organizations is a challenge. Various industry standards are attempting to resolve this issue. Once the problems are resolved, we can expect more user interest in these technologies.” “I agree that usability is more than just thinking about user-friendliness. Users today expect anytime anywhere access to information. This applies to PHRs as well.” |
Health policy analyst (digital health strategies) | eHealth initiatives and PHR adoption |
“There is a lot of work going on at the national and provincial levels to create the right conditions to support potential applications of PHR technologies.” “Suggestions made in this research can be useful in creating more awareness at the user level. Ultimately, we would like to see PHRs as a technology for all citizens.” |
aPHR: personal health record.
bEHR: electronic health record.
As an exploratory study, our research has inherent limitations in terms of the posited research model. This includes hypotheses that did not emerge as significant. Another limitation of our study pertains to the use of convenience and self-selection sampling techniques. This may limit the generalizability of the results of this study. Furthermore, most of the respondents comprised a relatively younger age demographic from North America, and the results may not be representative of the general population.
We also note that by virtue of soliciting responses from a current PHR portal site, health information websites, and forums, our data were collected from respondents with some level of previous interest in health self-management. This limits our findings to current internet users with potentially higher health literacy and may not accurately account for the population of users with less exposure to health information or with less access to computing resources. Future research should include potential and actual users of PHR technologies through more diversified sources and utilize recruitment mechanisms to alleviate sampling bias.
Advancing the use of technologies in all walks of life is also increasing people’s expectations of user-centered health care technologies. Consequently, consumer demand for PHR systems is likely to remain strong in the upcoming years. Recent academic and industry research on PHR systems has affirmed abundant consumer interest in these technologies [
The empirical research findings reported in this paper aim to contribute to the body of knowledge on consumer adoption of PHRs. To this end, we have attempted to explore and analyze possible factors contributing to what has been termed the
By developing and validating a parsimonious research model comprising personal and technological determinants of PHR adoption, we were able to obtain several insights into the social influence and cognitive instrumental processes that impact consumer adoption of PHRs. Our results indicate that subjective norms, technology awareness, and technology anxiety are important factors that predict individual attitudes and beliefs about the usefulness of PHR systems and the ultimate adoption of these technologies. Our study also shows the differential effects of system integration capabilities and perceived usability on perceived usefulness and behavioral intention to adopt PHRs. Our characterization of PHR technologies in terms of their voluntary, instrumental, and high maintenance attributes has allowed us to make sense of some of the seemingly counterintuitive findings about technology antecedents of PHR adoption.
As such, our findings support the viewpoint of other researchers who contend that PHR technologies are complex innovations in which perceived attributes of technology are neither stable features nor sure determinants of adoption [
We hope that the takeaways from our study will prove to be constructive in helping align PHR offerings more closely with consumer beliefs and attitudes, as well as their informational needs and functional requirements. This should help alleviate the risk of PHR technology rejection or abandonment.
Literature review summary.
Psychometric scales and measurement indicators.
Summary of responses to technographic questions.
Measurement and structural model assessment.
average variance extracted
consumer health informatics
electronic health record
electronic medical record
goodness of fit
health information system
information system
personal health record
partial least squares
structural equation modeling
technology acceptance model
None declared.