Natural-Setting PHR Usability Evaluation using the NASA TLX to Measure Cognitive Load of Patients

While personal health records (PHRs) carry an array of potential benefits such as increased patient engagement, poor usability remains a significant barrier to patients’ adoption of PHRs. In this mixed methods study, we evaluate the usability of one PHR feature, an intake form called the pre-visit summary, from the perspective of cognitive load using real cardiovascular patients in a natural setting. A validated measure for cognitive load, the NASA Task Load Index, was used along with retrospective interviews to identify tasks within the pre-visit summary that increased participants’ cognitive load. We found that the medications, immunizations, active health concerns, and family history pages induced a higher cognitive load because participants struggled to recall personal health information and also due to user interface design issues. This research is significant in that it uses validated measures of cognitive load to study real patients interacting with their PHR in a natural environment

Factors Affecting Patients’ Use of Electronic Personal Health Records in England: Cross-Sectional Study

Background: Electronic 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. Objective: This study aimed to examine factors associated with patients’ use of ePHRs in England. Methods: The 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. Results: Of 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. Conclusions: This 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

Factors Affecting the Behavioral Intention to Use Standalone Electronic Personal Health Record Applications by Adults in Egypt

Standalone electronic personal health record can be a useful tool that enables individuals to store, arrange and share their health information easily and they can build a history of their health timeline which is crucial for raising healthcare quality and better self-management, the adoption rate of these applications has been identified in several countries to be low and slowly progressing.Although there are some applications of standalone ePHR available in the market for usage free of charge but it’s almost not adopted at all, this study will investigate some of the factors that might affect the adoption of ePHR technology by adults in Egypt and provide business professionals a better picture for what can motivate or hinder the adoption process to achieve better adoption rates and eliminate the barriers.In order to ensure a comprehensive contextual analysis, researchers analyzed the research in hand with the perspective of the proposed contextual framework, the Nine Elements Framework/Model (Elsafty, 2018) that analyzes social studies research in general, and business/management reseaerches as well.Using the nine elements framework, the authors used it to discover the underlying factors that are causing the problems faced by the research in hand, and resulted in the coming contextual analysis defining the research scope and focus, which in the case of this paper is on Perceived usefulness and perceived ease of use were adapted from TAM that was initially developed by Fred Davis (1989) and they proved to have a high predictive power of behavioral intention in CHI context, The extensions of TAM including UTAUT & UTAUT2 seems to be irrelevant to this research context since UTAUT is more oriented towards the organizational context (Venkatesh et al., 2012) and UTAUT2 added factors, Price value seems to be irrelevant in this research context as we are already studying platforms that are provided free of charge, Hedonic motivation maybe irrelevant to this context as healthcare related service is mostly associated with seriousness and urgency, also testing unimplemented platforms that are not yet adopted makes from the habit unrealistic experience that may be inaccurate to measure.Since other several researches recommended extending these factors with other additional factors to make it more relevant to the healthcare consumer context (Kim & Park, 2012), these factors may include health-related factors, technology-related factors and personal-related factors. Findings in this research revealed that adoption rate in Egypt is still very low and high demand for this service which makes this research is significant as it’s trying to find out the reasons behind this gap, perceived usefulness, perceived ease of use, (privacy and security), eHealth literacy, personalization and awareness had a significant impact on behavioral intention to use standalone ePHR applications. Personalization was found to have the strongest effect on behavioral intention followed by perceived usefulness. Health status was found to have an insignificant effect on behavioral intention which indicates the interest of people with different health statuses in standalone ePHR

Patients’ Adoption of Electronic Personal Health Records in England: Secondary data analysis

Background: In 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. Objective: This study aims to improve the predictive power of the Abd-Alrazaq model by proposing new relationships between the existing variables in the model. Methods: The 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. Results: The 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. Conclusions: The 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

Factors Affecting Patients’ Use of Electronic Personal Health Records

England has recently introduced a nationwide electronic personal health record (ePHR) called Patient Online. Although ePHRs are widely available, adoption rates of ePHRs are usually low. Understanding the factors affecting patients’ use of ePHRs is considered important to increase adoption rates and improve the implementation success of ePHRs. Therefore, the current study aims to examine the factors that affect patients’ adoption of ePHRs in England. A systematic review was conducted to identify factors that affect patients’ adoption of ePHRs. Then, the most common theories and models relevant to technology adoption and human behaviour were reviewed to select an appropriate theory and use it as a theoretical lens for examining the factors in the current study. The Unified Theory of Acceptance and Use of Technology (UTAUT) was selected and tailored to the context of ePHRs by including the most influential factors identified by the systematic review. A cross-sectional survey of 624 patients in four general practices in West Yorkshire was carried out to empirically examine the proposed model via structural equation modelling. The results showed that performance expectancy, effort expectancy, and perceived privacy and security were significant predictors of behavioural intention. The relationship between social influence and behavioural intention was not statistically significant. Both facilitating conditions and behavioural intention affected use behaviour. Performance expectancy was also a significant mediator of the effect of both effort expectancy and perceived privacy and security on behavioural intention. Eleven relationships were moderated by age, sex, income, education, ethnicity, and internet access. The proposed model accounted for 76% and 48% of the variance in behavioural intention and use behaviour, respectively. The current study makes a significant contribution by adapting and validating a theoretical model (UTAUT) in a new context (ePHRs). Further, this study contributes to practices by providing several implications for developers, marketers, and GP practices