ArticleJournal of medical Internet research2020
Patients' Adoption of Electronic Personal Health Records in England: Secondary Data Analysis.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Women's experiences of using patient portals in healthcare settings: a rapid review.BMC women's health · 2024Pooled it
- Machine learning models for predicting postpartum convulsions using clinical indicators from PMA Ethiopia data.Scientific reports · 2026Article
- Machine learning-based prediction of fever among under-five children in Ethiopia: A national-level study.PloS one · 2026Article
- Patient Health Record Smart Network Challenges and Trends for a Smarter World.Sensors (Basel, Switzerland) · 2025Review
- Patient Adoption of Digital Use Cases in Family Medicine and a Nuanced Implementation Approach for Family Doctors: Quantitative Web-Based Survey Study.JMIR formative research · 2025Article
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- Employing supervised machine learning algorithms for classification and prediction of anemia among youth girls in Ethiopia.Scientific reports · 2024Article
- Knowledge, Attitudes, and Practices about Electronic Personal Health Records: A Cross-Sectional Study in a Region of Northern Italy.Journal of medical systems · 2024Article
- Predicting Health Care Providers' Acceptance of a Personal Health Record Secure Messaging Feature.Applied clinical informatics · 2022Article
- Human-Technology Interaction Factors Associated With the Use of Electronic Personal Health Records Among Younger and Older Adults: Secondary Data Analysis.Journal of medical Internet research · 2021Article
- Health Care Providers' Acceptance of a Personal Health Record: Cross-sectional Study.Journal of medical Internet research · 2021Article
- Predicting Patients' Intention to Use a Personal Health Record Using an Adapted Unified Theory of Acceptance and Use of Technology Model: Secondary Data Analysis.JMIR medical informatics · 2021Article
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6 authors.
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Abstract
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.
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