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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Dec 17, 2019
Date Accepted: Mar 11, 2020

The final, peer-reviewed published version of this preprint can be found here:

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

Abd-Alrazaq A, Alalwan AA, McMillan B, Bewick BM, Househ M, AL-Zyadat AT

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

J Med Internet Res 2020;22(10):e17499

DOI: 10.2196/17499

PMID: 33026353

PMCID: 7578819

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

  • Alaa Abd-Alrazaq; 
  • Ali Abdallah Alalwan; 
  • Brian McMillan; 
  • Bridgette M Bewick; 
  • Mowafa Househ; 
  • Alaa T AL-Zyadat

ABSTRACT

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 EE, PPS, and SI on BI were statistically stronger for females without internet access, patients with internet access, and patients without internet access, respectively.

Conclusions:

The predictive power of Abd-alrazaq’s model slightly improved by virtue of some new associations proposed in the model. 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.


 Citation

Please cite as:

Abd-Alrazaq A, Alalwan AA, McMillan B, Bewick BM, Househ M, AL-Zyadat AT

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

J Med Internet Res 2020;22(10):e17499

DOI: 10.2196/17499

PMID: 33026353

PMCID: 7578819

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