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A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model. | LitMetric

AI Article Synopsis

  • The study aims to understand the factors influencing the adoption of electronic personal health records (ePHRs) for Alzheimer's care by gathering insights from care providers and caregivers.
  • Conducted in Kerman, Iran, the research involved interviews with neurologists and caregivers, focusing on their experiences and perceptions during the COVID-19 pandemic.
  • The findings revealed that factors like performance expectancy, effort expectancy, social influence, and demographic traits impacted ePHR adoption, with participants generally viewing the technology as efficient but facing challenges like privacy concerns.

Article Abstract

Background And Aims: As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care.

Methods: This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis.

Results: The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment.

Conclusion: The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323167PMC
http://dx.doi.org/10.1002/hsr2.1394DOI Listing

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