New Integrated Model Approach to Understand the Factors That Drive Electronic Health Record Portal Adoption: Cross-Sectional National Survey

Tavares, Jorge; Oliveira, Tiago

doi:10.2196/11032

Cited by 59 publications

(56 citation statements)

References 60 publications

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“…We found that the perceived usefulness of EPP mostly drives the intent to use. Such a finding is echoed in other studies using the TAM model [81][82][83]. To bolster EPP use, individuals need to be able to understand the purpose of the technology, which is to provide information when and where it is needed to improve outcomes and patient safety.…”

Section: Discussionmentioning

confidence: 94%

Users’ acceptance of electronic patient portals in Lebanon

Honein-AbouHaidar

Antoun

Badr

et al. 2020

BMC Med Inform Decis Mak

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Background: Acceptance of Electronic patient portal (EPP) is instrumental for its success. Studies on users' acceptance in the Middle East region are scarce. This study aims to use the TAM as a framework to quantitatively describe potential users, diabetic and chronic high blood pressure patients and their providers, intention to use and factors influencing the intention to use EPP at AUBMC-FMC We concurrently test the internal construct validity and the reliability of the TAM. Methods: A cross-sectional survey design and the vignette approach were used. For validation, we needed a minimum of 180 patients; all 35 attending physicians and 11 registered nurses were targeted. We used descriptive statistics to calculate the intention to use EPP and its determinants based on the TAM constructs. Exploratory factor analysis (EFA) and structural equation modeling (SEM) were employed to estimate significant path coefficients for patients only as the sample size of providers was too small. Results: We had 199 patients, half intended to use EPP; 73% of providers (N = 17) intended to use EPP. Perceived ease of use and privacy concerns were significantly higher among providers than patients (Mean (M) = 0.77 vs M = 0.42 (CI: − 0.623; − 0.081)) and (M = 3.67 vs M = 2.13, CI: − 2.16; − 0.91) respectively; other constructs were not significantly different. Reliability of TAM revealed a Cronbach Alpha of α=.91. EFA showed that three components explained 73.48% of the variance: Behavioral Intention of Use (14.9%), Perceived Ease of Use (50.74%), Perceived Usefulness (7.84%). SEM found that perceived ease of use increased perceived usefulness (standardized regression weight = 0.49); perceived usefulness (0.51) had more predictive value than perceived ease of use (0.27) to explain the behavioral intention of use of the EPP. Conclusions: We found that providers valued the usefulness of EPP and were mostly intending to use it. This finding has yet to be tested in future studies testing actual use as intention and actual use may not be concordant. The intention to use among patients was lower than those reported in developed countries. We identified two factors that we need to address to increase use, namely perceived ease and usefulness, and proposed practical implications to address them; future research directions were also discussed.

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Section: Discussionmentioning

confidence: 94%

Users’ acceptance of electronic patient portals in Lebanon

Honein-AbouHaidar

Antoun

Badr

et al. 2020

BMC Med Inform Decis Mak

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“…UTAUT2 has already shown good results when applied to the health care context, with the addition of specific health-related constructs [ 22 - 24 ]. Concerning specific health care adoption models, the aim is to achieve an R 2 value of at least 50% [ 21 , 25 ], except when the model deals with constructs related to sensitive topics such as confidentiality or patient behavior in which an R 2 of 20% for the critical dependent variables can be regarded as acceptable [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Performance expectancy is defined as the consumers’ projections of benefits provided by the use of technology and is a good predictor of behavior intention, including in the eHealth context [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Salgado¹,

Tavares²,

Oliveira³

2020

JMIR Mhealth Uhealth

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Background Mobile health (mHealth) has potential to play a significant role in realizing a reversal of the current paradigm in health care toward a more patient-centric and more collaborative system to improve the outcomes obtained along with the quality and sustainability of health care systems. Objective The aim of this study was to explore and understand individual mHealth acceptance drivers between two groups of users: those with chronic health conditions and those without. Methods The extended unified theory of acceptance and usage of technology (UTAUT2) was enhanced with a new health-related framework: behavior intention to recommend and new mediation effects. We applied partial least squares (PLS) causal modeling to test the research model. Results We obtained 322 valid responses through an online questionnaire. The drivers of behavior intention with statistical significance were performance expectancy (β=.29, P<.001), habit (β=.39, P<.001), and personal empowerment (β=.18, P=.01). The precursors of use behavior were habit (β= .47, P<.001) and personal empowerment (β=.17, P=.01). Behavior intention to recommend was significantly influenced by behavior intention (β=.58, P<.001) and personal empowerment (β=.26, P<.001). The model explained 66% of the total variance in behavior intention, 54% of the variance in use behavior, and 70% of the variance in behavior intention to recommend. Conclusions Our study demonstrates a significant role of personal empowerment, as a second-order construct, in the mHealth acceptance context. The presence of a chronic health condition predicates an impact on acceptance of this technology.

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“…Firms must deliver responsible innovations in health and this technology policy scale is expected to support this objective (Lehoux et al, 2019). Medical data captured in a scientific manner can be utilized for enabling technology for creation of electronic health records that are critical for risk management (Tavares & Oliveira, 2018). This can be further integrated with machine learning that can be used to promote healthy lifestyles and disease prevention strategies to strengthen universal health care (Ashrafian & Darzi, 2018).…”

Section: Discussionmentioning

confidence: 99%

Democratizing health insurance services; accelerating social inclusion through technology policy of health insurance firms

2019

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Technology plays an important role in reducing inequalities in health care delivery and enhancing quality of life. Health insurance firms require a potent technology policy to understand social determinants of health insurance that can lead to more innovative approaches to social inclusivity initiatives. This paper explicates critical factors of technology policy of health insurance firms that can contribute to better social inclusion and move towards universal health care. Through structured survey questionnaire, data were collected from 125 senior health insurance executives from India for identifying factors important for technology policy. A technology policy scale comprising six distinct factors is developed for health insurance firms using exploratory factor analysis. The factors are “organizational technology strategy,” “technology value drivers,” “technology utilization,” “firm agility in technology,” “firm technology capabilities,” and “customer focus through technology.” The factors identified in this study can be utilized and integrated with enabling technologies such as machine learning, internet of things, big data analytics, and digital phenotyping for creation of electronic health records that are critical for risk management of health insurance firms. This would help to promote healthy lifestyles and disease prevention strategies to strengthen universal health care for society. This paper looks at the factors that are essential in framing a technology policy for the health insurance sector. It provides reasons on how the factors contribute in the evolving health insurance field and can increase the rate of social inclusion for health care. And, finally, it helps in distinguishing how technology requirements and utilization in health insurance is different from other businesses.

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New Integrated Model Approach to Understand the Factors That Drive Electronic Health Record Portal Adoption: Cross-Sectional National Survey

Cited by 59 publications

References 60 publications

Users’ acceptance of electronic patient portals in Lebanon

Users’ acceptance of electronic patient portals in Lebanon

Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Democratizing health insurance services; accelerating social inclusion through technology policy of health insurance firms

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