Patients’ Adoption of Electronic Personal Health Records in England: Secondary Data Analysis

Abstract

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.

Human-Technology Interaction Factors Associated With the Use of Electronic Personal Health Records Among Younger and Older Adults: Secondary Data Analysis

“…Another human-technology interaction factor, ease of understanding health information in ePHRs, was found to be significantly linked to ePHR use among older adults but not younger adults. This finding is in line with Abd-Alrazaq et al’s [ 29 , 30 ] studies, which suggested that perceived ease of use is positively associated with the intention to use ePHRs, with this relationship being stronger among older patients. This difference might be explained by the lower health literacy among older adults compared to their younger counterparts [ 36 ].…”

“…The study also found that there was no significant difference in average ePHR use between the 2 age groups, which contradicts the findings of Greenberg et al [ 11 ] and McInnes et al [ 12 ], who reported younger age to be related to higher ePHR use. Including performance expectancy, effort expectancy, social influence, and facilitating conditions as independent variables, 2 studies conducted by Abd-Alrazaq and colleagues [ 29 , 30 ] found that age moderated the effects of performance expectancy, effort expectancy, and facilitating conditions on intention to use ePHRs. The moderating effect of age might be able to explain the nonsignificant finding of age difference in our study, which suggests that future studies are needed to explore the moderating effect of age using the current data set and measurements.…”

Predicting Patients’ Intention to Use a Personal Health Record Using an Adapted Unified Theory of Acceptance and Use of Technology Model: Secondary Data Analysis

“…Other variables acting as either mediators or moderators may help enrich our understanding of PHR adoption within this context. Abd-Alrazaq et al [ 47 ] developed the Abd-Alrazaq Model to examine mediating, moderating, and moderated mediating effects on patients’ behavioral intention to use a PHR in England.…”

Health Care Providers’ Acceptance of a Personal Health Record: Cross-sectional Study

“…The original UTAUT explained 76% of the variance in behavioral intention. No studies on PHR adoption have used the original UTAUT model [ 50 , 65 , 66 , 69 , 70 ]. The advantage of the adapted model is a similar predictive power while parsimoniously eliminating the construct “use behavior” and the moderator “voluntariness” in the original model.…”

“…Consequently, they will be more inclined to recommend PHRs to patients. Through their interactions with HCPs, patients will perceive PHRs as useful and are more likely to adopt them [ 66 ].…”