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; 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 and colleagues adopted a model to assess factors that influence patients’ use of GP online services in England. According to the previous literature, we believed that the predictive power of Abd-alrazaq’s model could be improved by proposing new associations between existing variables in the model.
Objective: The current study aims to improve the predictive power of Abd-alrazaq’s model through proposing new relationships between existing variables in the model.
Methods: Abd-alrazaq’s model was amended by proposing new direct, mediating, moderating, and moderated mediating effects. The amended model was examined using data collected in the previous study. That data were collected by a cross-sectional survey of a convenience sample in four GPs in West Yorkshire, England. Structural equation modelling 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 behavioural intention (BI), and 49% of the variance in use behaviour (UB). In addition to the significant associations found in the previous study, the current study found that social influence (SI) and facilitating conditions (FC) are associated with PE directly and BI indirectly through PE. The association between BI and UB was stronger for younger females with higher level of education and income and internet access. The indirect effects of effort expectancy (EE), perceived privacy and security (PPS), and SI on BI were statistically stronger for females without internet access, patients with internet access, and patients without internet access, respectively. While the indirect effect of PPS on BI was stronger for patients with college or diploma compared to those with secondary school and lower, the indirect effect of EE on BI was stronger for patients with secondary school or lower compared to those with college or diploma.
Conclusions: The predictive power of Abd-alrazaq’s model improved by virtue of new significant associations that were not examined before in the ePHRs context. 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 taken into account for improving patients’ use of ePHRs.