Factors that affect the use of electronic personal health records among patients: A systematic review

Abd‐Alrazaq, Alaa; Bewick, Bridgette M.; Farragher, Tracey; Gardner, Peter

doi:10.1016/j.ijmedinf.2019.03.014

Cited by 101 publications

(129 citation statements)

References 110 publications

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“…To help implement our system into practice, we will perform further longitudinal acceptance modeling studies on the full version of our system to focus on the factors that affect older adults’ and health care professionals’ acceptance [ 70 ]. Following that, targeted strategies (eg, community-based technology support services and training workshops) will be promoted to improve user acceptance on our smart system.…”

Section: Discussionmentioning

confidence: 99%

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

Wang¹,

Zhao²,

Yu³

et al. 2020

J Med Internet Res

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Background Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. Methods The system was designed to capture and record older adults’ health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users’ attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. Results A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants’ daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). Conclusions The proposed health monitoring system provides an example design for monitoring older adults’ health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.

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Section: Discussionmentioning

confidence: 99%

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

Wang¹,

Zhao²,

Yu³

et al. 2020

J Med Internet Res

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“…Adoption of healthcare informatics generally and ePHR specifically from consumers perspective is a complicated process that has been identified by many factors (Najaftorkaman, 2016;Showell, 2017;Ozok et al, 2017;Abd-alrazaq et al, 2019a). In USA, a systematic review study was reviewing the factors affecting the adoption intention to use ePHR, around 59 factors related to intention to use PHR has been detected in the reviewed studies which shows how the study of this subject is very complex and affected by many variables, also the factors that affect the initial intention to use may be different from others that influence the continuity of use (Abd-alrazaq et al, 2019a).…”

Section: Factors Affecting Adoption Of Ephrmentioning

confidence: 99%

“…Despite the enormous benefits of ePHR systems but its adoption has been low in several countries including developing and developed countries (Ford et al, 2016;Katehakis et al, 2018), also its dispersion rate is slowly growing (Ford et al, 2016;Katehakis et al, 2018), many studies discussed several factors that can affect the adoption of using such systems and it was found that many factors can affect the adoption process (Abd-alrazaq et al, 2019a). these factors were either motivating or hindering the adoption process, knowing the true predictors of behavioral intention to use these systems is crucial for achieving success in making ePHR systems more usable and have a widespread in different countries.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Behavioral Intention to Use Standalone Electronic Personal Health Record Applications by Adults in Egypt

Elsafty

Elbouseery²,

Shaarawy³

2020

BMS

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Standalone electronic personal health record can be a useful tool that enables individuals to store, arrange and share their health information easily and they can build a history of their health timeline which is crucial for raising healthcare quality and better self-management, the adoption rate of these applications has been identified in several countries to be low and slowly progressing.Although there are some applications of standalone ePHR available in the market for usage free of charge but it’s almost not adopted at all, this study will investigate some of the factors that might affect the adoption of ePHR technology by adults in Egypt and provide business professionals a better picture for what can motivate or hinder the adoption process to achieve better adoption rates and eliminate the barriers.In order to ensure a comprehensive contextual analysis, researchers analyzed the research in hand with the perspective of the proposed contextual framework, the Nine Elements Framework/Model (Elsafty, 2018) that analyzes social studies research in general, and business/management reseaerches as well.Using the nine elements framework, the authors used it to discover the underlying factors that are causing the problems faced by the research in hand, and resulted in the coming contextual analysis defining the research scope and focus, which in the case of this paper is on Perceived usefulness and perceived ease of use were adapted from TAM that was initially developed by Fred Davis (1989) and they proved to have a high predictive power of behavioral intention in CHI context, The extensions of TAM including UTAUT & UTAUT2 seems to be irrelevant to this research context since UTAUT is more oriented towards the organizational context (Venkatesh et al., 2012) and UTAUT2 added factors, Price value seems to be irrelevant in this research context as we are already studying platforms that are provided free of charge, Hedonic motivation maybe irrelevant to this context as healthcare related service is mostly associated with seriousness and urgency, also testing unimplemented platforms that are not yet adopted makes from the habit unrealistic experience that may be inaccurate to measure.Since other several researches recommended extending these factors with other additional factors to make it more relevant to the healthcare consumer context (Kim & Park, 2012), these factors may include health-related factors, technology-related factors and personal-related factors. Findings in this research revealed that adoption rate in Egypt is still very low and high demand for this service which makes this research is significant as it’s trying to find out the reasons behind this gap, perceived usefulness, perceived ease of use, (privacy and security), eHealth literacy, personalization and awareness had a significant impact on behavioral intention to use standalone ePHR applications. Personalization was found to have the strongest effect on behavioral intention followed by perceived usefulness. Health status was found to have an insignificant effect on behavioral intention which indicates the interest of people with different health statuses in standalone ePHR.

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“…Identifying and understanding factors that affect patients’ use of ePHRs is crucial to develop interventions to increase patients’ adoption and improve the implementation success of ePHRs [17-22]. According to a systematic review conducted by Abd-alrazaq and colleagues [23], there are no published studies on factors affecting patients’ use of ePHRs in England. Although many studies have been conducted in other countries, they have several shortcomings, namely, (1) few studies were theory-based research [21,24-27], (2) many studies focused on factors that affect patients’ intention to use ePHRs instead of actual use [28-32], (3) many studies have assessed the factors that affect self-reported use rather than actual use [27,32-35], (4) almost all studies examined independent and dependent variables at one point in time using the same data collection instrument, so being at risk of common method bias [25,32,36], and (5) almost all studies did not differentiate between factors affecting initial use and continuing use of ePHRs.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Patients’ Use of Electronic Personal Health Records in England: Cross-Sectional Study

Abd‐Alrazaq¹,

Bewick²,

Farragher³

et al. 2019

J Med Internet Res

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BackgroundElectronic personal health records (ePHRs) are secure Web-based tools that enable individuals to access, manage, and share their medical records. England recently introduced a nationwide ePHR called Patient Online. As with ePHRs in other countries, adoption rates of Patient Online remain low. Understanding factors affecting patients’ ePHR use is important to increase adoption rates and improve the implementation success of ePHRs.ObjectiveThis study aimed to examine factors associated with patients’ use of ePHRs in England.MethodsThe unified theory of acceptance and use of technology was adapted to the use of ePHRs. To empirically examine the adapted model, a cross-sectional survey of a convenience sample was carried out in 4 general practices in West Yorkshire, England. Factors associated with the use of ePHRs were explored using structural equation modeling.ResultsOf 800 eligible patients invited to take part in the survey, 624 (78.0%) returned a valid questionnaire. Behavioral intention (BI) was significantly influenced by performance expectancy (PE; beta=.57, P<.001), effort expectancy (EE; beta=.16, P<.001), and perceived privacy and security (PPS; beta=.24, P<.001). The path from social influence to BI was not significant (beta=.03, P=.18). Facilitating conditions (FC) and BI significantly influenced use behavior (UB; beta=.25, P<.001 and beta=.53, P<.001, respectively). PE significantly mediated the effect of EE and PPS on BI (beta=.19, P<.001 and beta=.28, P=.001, respectively). Age significantly moderated 3 paths: PE→BI, EE→BI, and FC→UB. Sex significantly moderated only the relationship between PE and BI. A total of 2 paths were significantly moderated by education and internet access: EE→BI and FC→UB. Income moderated the relationship between FC and UB. The adapted model accounted for 51% of the variance in PE, 76% of the variance in BI, and 48% of the variance in UB.ConclusionsThis study identified the main factors that affect patients’ use of ePHRs in England, which should be taken into account for the successful implementation of these systems. For example, developers of ePHRs should involve patients in the process of designing the system to consider functions and features that fit patients’ preferences and skills to ensure systems are useful and easy to use. The proposed model accounted for 48% of the variance in UB, indicating the existence of other, as yet unidentified, factors that influence the adoption of ePHRs. Future studies should confirm the effect of the factors included in this model and identify additional factors.

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Factors that affect the use of electronic personal health records among patients: A systematic review

Cited by 101 publications

References 110 publications

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

Factors Affecting the Behavioral Intention to Use Standalone Electronic Personal Health Record Applications by Adults in Egypt

Factors Affecting Patients’ Use of Electronic Personal Health Records in England: Cross-Sectional Study

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