Factors influencing consumer adoption of USB-based Personal Health Records in Taiwan.
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ABSTRACT:
SUBMITTER: Jian WS
PROVIDER: S-EPMC3465237 | biostudies-literature | 2012 Aug
REPOSITORIES: biostudies-literature
Publications
Factors influencing consumer adoption of USB-based Personal Health Records in Taiwan.
Jian Wen-Shan WS Syed-Abdul Shabbir S Sood Sanjay P SP Lee Peisan P Hsu Min-Huei MH Ho Cheng-Hsun CH Li Yu-Chuan YC Wen Hsyien-Chia HC
BMC health services research 20120827
<h4>Background</h4>Usually patients receive healthcare services from multiple hospitals, and consequently their healthcare data are dispersed over many facilities' paper and electronic-based record systems. Therefore, many countries have encouraged the research on data interoperability, access, and patient authorization. This study is an important part of a national project to build an information exchange environment for cross-hospital digital medical records carried out by the Department of He ...[more]
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Project description:BackgroundIncreasingly 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.ObjectiveThis paper reports findings from an empirical investigation of the sociotechnical factors affecting the adoption of PHRs.MethodsA 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.ResultsThe 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.ConclusionsWe 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.
| S-EPMC8501412 | biostudies-literature
Project description:BACKGROUND:Personal Health Records (PHR) systems provide individuals with access and control over their health information and consequently can support individuals in becoming active participants, rather than passive recipients, in their own care process. In spite of numerous benefits suggested for consumers' utilizing PHR systems, research has shown that such systems are not yet widely adopted or well known to consumers. Bearing in mind the potential benefits of PHRs to consumers and their potential interest in these systems-and that similar to any other type of information system, adoption is a prerequisite for realizing the potential benefits of PHR systems-research is needed to understand how to enhance the adoption rates for PHR systems. OBJECTIVE:This research seeks to understand how individuals' intentions to adopt PHR systems are affected by their self-determination in managing their own health-the extent of their ability to take an active role in managing their own health. As such, this research aims to develop and empirically validate a theoretical model that explains PHR systems adoption by the general public through the integration of theories from the information systems and psychology literatures. METHODS:This research employs a cross-sectional survey method targeted at the Canadian general public without any prior experience in using PHR systems. A partial least squares approach to structural equation modeling was used to validate the proposed research model of this study (N=159). RESULTS:Individuals with higher levels of ability to manage their own health (self-determination) are more likely to adopt PHR systems since they have more positive perceptions regarding the use of such systems. Further, such self-determination is fueled by autonomy support from consumers' physicians as well as the consumers' personality trait of autonomy orientation. CONCLUSIONS:This study advances our theoretical understanding of PHR systems adoption. It also contributes to practice by providing insightful implications for designing, promoting, and facilitating the use of PHR systems among consumers.
| S-EPMC5553007 | biostudies-literature
Project description:BackgroundIn England, almost all general practices (GPs) have implemented GP online services such as electronic personal health records (ePHRs) that allow people to schedule appointments, request repeat prescriptions, and access parts of their medical records. The overall adoption rate of GP online services has been low, reaching just 28% in October 2019. In a previous study, Abd-Alrazaq et al adopted a model to assess the factors that influence patients' use of GP online services in England. According to the previous literature, the predictive power of the Abd-Alrazaq model could be improved by proposing new associations between the existing variables in the model.ObjectiveThis study aims to improve the predictive power of the Abd-Alrazaq model by proposing new relationships between the existing variables in the model.MethodsThe Abd-Alrazaq model was amended by proposing new direct, mediating, moderating, and moderated mediating effects. The amended model was examined using data from a previous study, which were collected by a cross-sectional survey of a convenience sample of 4 GPs in West Yorkshire, England. Structural equation modeling was used to examine the theoretical model and hypotheses.ResultsThe new model accounted for 53% of the variance in performance expectancy (PE), 76% of the variance in behavioral intention (BI), and 49% of the variance in use behavior (UB). In addition to the significant associations found in the previous study, this study found that social influence (SI) and facilitating conditions (FCs) are associated with PE directly and BI indirectly through PE. The association between BI and UB was stronger for younger women with higher levels of education, income, and internet access. The indirect effects of effort expectancy (EE), perceived privacy and security (PPS), and SI on BI were statistically stronger for women without internet access, patients with internet access, and patients without internet access, respectively. The indirect effect of PPS on BI was stronger for patients with college education or diploma than for those with secondary school education and lower, whereas the indirect effect of EE on BI was stronger for patients with secondary school education or lower than for those with college education or a diploma.ConclusionsThe predictive power of the Abd-Alrazaq model improved by virtue of new significant associations that were not examined before in the context of ePHRs. Further studies are required to validate the new model in different contexts and to improve its predictive power by proposing new variables. The influential factors found in this study should be considered to improve patients' use of ePHRs.
| S-EPMC7578819 | biostudies-literature
Project description:Given the widespread adoption of electronic medical records and recent emergence of electronic family history tools, we examined genetic counselors' perspectives on the emerging technology of the personal health record (PHR)-based family history tool that links to an electronic medical record (EMR). Two-hundred thirty-three genetic counselors responded to an on-line survey eliciting current use of electronic family history (EFH) tools and familiarity with PHR-based family history tools. Additionally, after being shown a series of screen shots of a newly developed PHR-based family history tool based on the U.S. Surgeon General's My Family Health Portrait (United States Department of Health and Human Services 2009), participants were surveyed about the perceived usefulness, ease of use, and impact on current workflow that this kind of tool would have in their practices. Eighty-three percent reported that their institution has an EMR, yet only 35 % have a dedicated space for family history. Eighty-two percent reported that less than 5 % of their patients have a PHR, and only 16 % have worked with patients who have a PHR. Seventy-two percent or more agreed that a PHR-based family history tool would facilitate communication, increase accuracy of information, ensure consistency in recording information, increase focus on actual counseling, reduce repetitive questions, improve efficiency, and increase the legibility and clarity. Our findings suggest that participants were familiar with existing EFH tools, but that the majority did not use them in practice. Genetic counselors' adoption of such tools is limited due to non-existence of this kind of technology or inability to integrate it into their clinics. They are also strongly in favor of adopting a PHR-based family history tool in genetics clinics, but have practical concerns that must be addressed before the tool can be implemented.
| S-EPMC4882761 | biostudies-literature
Project description:BackgroundBlockchain technology has the potential to enable more secure, transparent, and equitable data management. In the health care domain, it has been applied most frequently to electronic health records. In addition to securely managing data, blockchain has significant advantages in distributing data access, control, and ownership to end users. Due to this attribute, among others, the use of blockchain to power personal health records (PHRs) is especially appealing.ObjectiveThis review aims to examine the current landscape, design choices, limitations, and future directions of blockchain-based PHRs.MethodsAdopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, a cross-disciplinary systematic review was performed in July 2020 on all eligible articles, including gray literature, from the following 8 databases: ACM, IEEE Xplore, MEDLINE, ScienceDirect, Scopus, SpringerLink, Web of Science, and Google Scholar. Three reviewers independently performed a full-text review and data abstraction using a standardized data collection form.ResultsA total of 58 articles met the inclusion criteria. In the review, we found that the blockchain PHR space has matured over the past 5 years, from purely conceptual ideas initially to an increasing trend of publications describing prototypes and even implementations. Although the eventual application of blockchain in PHRs is intended for the health care industry, the majority of the articles were found in engineering or computer science publications. Among the blockchain PHRs described, permissioned blockchains and off-chain storage were the most common design choices. Although 18 articles described a tethered blockchain PHR, all of them were at the conceptual stage.ConclusionsThis review revealed that although research interest in blockchain PHRs is increasing and that the space is maturing, this technology is still largely in the conceptual stage. Being the first systematic review on blockchain PHRs, this review should serve as a basis for future reviews to track the development of the space.
| S-EPMC8080150 | biostudies-literature
Project description:BackgroundThe application of telemedicine and electronic health (eHealth) technology has grown in importance during the COVID-19 pandemic, and a new approach in personal data management and processing MyData, has emerged. Data portability and informational self-determination are fundamental concepts of MyData. This study analysed the factors that influence acceptance of the MyData platform, which, reflects the right to self-determine personal data.MethodsThe study involved participants having experience using the MyData platform, and the key factors of the unified theory of acceptance and use of technology were used in the research model (performance expectancy, effort expectancy, social influence, facilitation condition and behavioural intention to use). The questionnaire comprided 27 items, and system usage log data were used to confirm that behavioural intention to use affected actual use behaviour through structural equation modeling.ResultsIn total, 1153 participants completed the survey. The goodness of fit in the structural equation model indices indicates that the data fit the research model well. Performance expectancy, social influence, and facilitating conditions had direct effects on behavioural intention to use. We used system usage log data to confirm that behavioural intention to use positively affected actual use behaviour. The impact of the main factors in the unified theory of acceptance and use of technology was not moderated by age or gender, except for performance expectancy.ConclusionsThis study is the first to examine the factors influencing the use of the MyData platform based on the personal health record data sharing system in Korea. In addition, the study confirmed the use behaviour of the MyData platform utilising the system's actual usage log for each function and analysing the effect of the intention of use on actual use. Our study serves as a significant foundation for the acceptance of data portability and sharing concepts. It also lays the foundation for expanding the data economy and ecosystem in the pandemic era.
| S-EPMC9283557 | biostudies-literature
Project description:BackgroundHealth care technologies can help improve workers' health and productivity by supporting workplace health promotion. A personal health record app is used to manage medical data such as results from medical checkups, which facilitates decision making for medical personnel. However, an analysis of users' technology acceptance is required to provide appropriate services based on personal health record apps.ObjectiveThe purpose of this study was to analyze the factors influencing the behavioral intention of health experts and workers to use an app in workers' health centers and to examine differences in their perception of the main variables.MethodsThe study involved health experts and workers who visited 21 workers' health centers in Korea to verify a research model in which perceived risk was added to the unified theory of acceptance and use of technology, a representative theory of information technology acceptance. After receiving ethical approval from the Korea National Institute for Bioethics Policy, 1050 questionnaires were distributed over 7 weeks with cooperation of the Korea Occupational Safety and Health Agency. A multiple linear regression analysis and multigroup path analysis were performed to verify the hypotheses, and independent samples t tests were performed to analyze differences between workers' and health experts' perception of the main variables.ResultsThe analysis included data from 866 respondents (687 workers and 179 health experts). Effort expectancy (beta=.08, P=.03), social influence (beta=.43, P<.001), performance expectancy (beta=.07, P=.008), and facilitating conditions (beta=.13, P<.001) exerted significant positive effects on behavioral intention, whereas perceived risk (beta=-.29, P<.001) exerted a significant negative effect on behavioral intention. Performance expectancy had a significant effect on path differences depending on gender (critical ratio=-3.38) and age (critical ratio=1.97). Workers' mean scores for the main variables were higher relative to those of health experts for all remaining variables except perceived risk, and significant differences were observed for all remaining variables except facilitating condition.ConclusionsSocial influence exerted the strongest effect on behavioral intention to use the personal health record app. Consequently, it is necessary to coordinate health promotion activities in the workplace as well as the operational direction of community institutions such as in workers' health centers to allow workers to manage their own health via continuous use of the app. In addition, the app should be developed based on a requirement analysis of the balance between both interest groups in consideration of differences in perspective between consumers and service providers.
| S-EPMC7303838 | biostudies-literature
Project description:BackgroundInformation and communication technology (ICT) has transformed the health care field worldwide. One of the main drivers of this change is the electronic health record (EHR). However, there are still open issues and challenges because the EHR usually reflects the partial view of a health care provider without the ability for patients to control or interact with their data. Furthermore, with the growth of mobile and ubiquitous computing, the number of records regarding personal health is increasing exponentially. This movement has been characterized as the Internet of Things (IoT), including the widespread development of wearable computing technology and assorted types of health-related sensors. This leads to the need for an integrated method of storing health-related data, defined as the personal health record (PHR), which could be used by health care providers and patients. This approach could combine EHRs with data gathered from sensors or other wearable computing devices. This unified view of patients' health could be shared with providers, who may not only use previous health-related records but also expand them with data resulting from their interactions. Another PHR advantage is that patients can interact with their health data, making decisions that may positively affect their health.ObjectiveThis work aimed to explore the recent literature related to PHRs by defining the taxonomy and identifying challenges and open questions. In addition, this study specifically sought to identify data types, standards, profiles, goals, methods, functions, and architecture with regard to PHRs.MethodsThe method to achieve these objectives consists of using the systematic literature review approach, which is guided by research questions using the population, intervention, comparison, outcome, and context (PICOC) criteria.ResultsAs a result, we reviewed more than 5000 scientific studies published in the last 10 years, selected the most significant approaches, and thoroughly surveyed the health care field related to PHRs. We developed an updated taxonomy and identified challenges, open questions, and current data types, related standards, main profiles, input strategies, goals, functions, and architectures of the PHR.ConclusionsAll of these results contribute to the achievement of a significant degree of coverage regarding the technology related to PHRs.
| S-EPMC5251169 | biostudies-literature
Project description:BackgroundElectronic personal health records (ePHRs) are defined as electronic applications through which individuals can access, manage, and share health information in a private, secure, and confidential environment. Existing evidence shows their benefits in improving outcomes, especially for chronic disease patients. However, their use has not been as widespread as expected partly due to barriers faced in their adoption and use. We aimed to identify the types of barriers to a patient, provider, and caregiver adoption/use of ePHRs and to analyze their extent in chronic disease care.MethodsA systematic search in Medline, PubMed, Science Direct, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Central Register of Controlled Trials, and the Institute of Electrical and Electronics Engineers (IEEE) database was performed to find original studies assessing barriers to ePHR adoption/use in chronic care until the end of 2018. Two researchers independently screened and extracted data. We used the PHR adoption model and the Unified Theory of Acceptance and Use of Technology to analyze the results. The Mixed Methods Appraisal Tool (MMAT) version 2018 was used to assess the quality of evidence in the included studies.ResultsSixty publications met our inclusion criteria. Issues found hindering ePHR adoption/use in chronic disease care were associated with demographic factors (e.g., patient age and gender) along with key variables related to health status, computer literacy, preferences for direct communication, and patient's strategy for coping with a chronic condition; as well as factors related to medical practice/environment (e.g., providers' lack of interest or resistance to adopting ePHRs due to workload, lack of reimbursement, and lack of user training); technological (e.g., concerns over privacy and security, interoperability with electronic health record systems, and lack of customized features for chronic conditions); and chronic disease characteristics (e.g., multiplicities of co-morbid conditions, settings, and providers involved in chronic care).ConclusionsePHRs can be meaningfully used in chronic disease care if they are implemented as a component of comprehensive care models specifically developed for this care. Our results provide insight into hurdles and barriers mitigating ePHR adoption/use in chronic disease care. A deeper understating of the interplay between these barriers will provide opportunities that can lead to an enhanced ePHR adoption/use.
| S-EPMC7341472 | biostudies-literature
Project description:BackgroundElectronic personal health records (ePHRs) are secure Web-based tools that enable individuals to access, manage, and share their medical records. England recently introduced a nationwide ePHR called Patient Online. As with ePHRs in other countries, adoption rates of Patient Online remain low. Understanding factors affecting patients' ePHR use is important to increase adoption rates and improve the implementation success of ePHRs.ObjectiveThis study aimed to examine factors associated with patients' use of ePHRs in England.MethodsThe unified theory of acceptance and use of technology was adapted to the use of ePHRs. To empirically examine the adapted model, a cross-sectional survey of a convenience sample was carried out in 4 general practices in West Yorkshire, England. Factors associated with the use of ePHRs were explored using structural equation modeling.ResultsOf 800 eligible patients invited to take part in the survey, 624 (78.0%) returned a valid questionnaire. Behavioral intention (BI) was significantly influenced by performance expectancy (PE; beta=.57, P<.001), effort expectancy (EE; beta=.16, P<.001), and perceived privacy and security (PPS; beta=.24, P<.001). The path from social influence to BI was not significant (beta=.03, P=.18). Facilitating conditions (FC) and BI significantly influenced use behavior (UB; beta=.25, P<.001 and beta=.53, P<.001, respectively). PE significantly mediated the effect of EE and PPS on BI (beta=.19, P<.001 and beta=.28, P=.001, respectively). Age significantly moderated 3 paths: PE→BI, EE→BI, and FC→UB. Sex significantly moderated only the relationship between PE and BI. A total of 2 paths were significantly moderated by education and internet access: EE→BI and FC→UB. Income moderated the relationship between FC and UB. The adapted model accounted for 51% of the variance in PE, 76% of the variance in BI, and 48% of the variance in UB.ConclusionsThis study identified the main factors that affect patients' use of ePHRs in England, which should be taken into account for the successful implementation of these systems. For example, developers of ePHRs should involve patients in the process of designing the system to consider functions and features that fit patients' preferences and skills to ensure systems are useful and easy to use. The proposed model accounted for 48% of the variance in UB, indicating the existence of other, as yet unidentified, factors that influence the adoption of ePHRs. Future studies should confirm the effect of the factors included in this model and identify additional factors.
| S-EPMC6693305 | biostudies-literature