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Tavares, J., & Oliveira, T. (2018). New Integrated Model Approach to Understand the Factors That Drive Electronic Health Record Portal Adoption: Cross-Sectional National Survey. Journal Of Medical Internet Research, 20(11), [e11032]. DOI: 10.2196/11032 BACKGROUND: The future of health care delivery is becoming more patient-focused, and electronic health record (EHR) portals are gaining more attention from worldwide governments that consider this technology as a valuable asset for the future sustainability of the national health care systems. Overall, this makes the adoption of EHR portals an important field to study. OBJECTIVE: The aim of this study is to understand the factors that drive individuals to adopt EHR portals. METHODS: We applied a new adoption model that combines 3 different theories, namely, extended unified theory of acceptance and use of technology, health belief model, and the diffusion of innovation; all the 3 theories provided relevant contributions for the understanding of EHR portals. To test the research model, we used the partial least squares causal modeling approach. We executed a national survey based on randomly generated mobile phone numbers. We collected 139 questionnaires. RESULTS: Performance expectancy (beta=.203; t=2.699), compatibility (beta=.530; t=6.189), and habit (beta=.251; t=2.660) have a statistically significant impact on behavior intention (R2=76.0%). Habit (beta=.378; t=3.821), self-perception (beta=.233; t=2.971), and behavior intention (beta=.263; t=2.379) have a statistically significant impact on use behavior (R2=61.8%). In addition, behavior intention (beta=.747; t=10.737) has a statistically significant impact on intention to recommend (R2=69.0%), results demonstrability (beta=.403; t=2.888) and compatibility (beta=.337; t=2.243) have a statistically significant impact on effort expectancy (R2=48.3%), and compatibility (beta=.594; t=6.141) has a statistically significant impact on performance expectancy (R2=42.7%). CONCLUSIONS: Our research model yields very good results, with relevant R2 in the most important dependent variables that help explain the adoption of EHR portals, behavior intention, and use behavior. publishersversion published

Tavares, J., Goulão, A., & Oliveira, T. (2018). Electronic Health Record Portals adoption: Empirical model based on UTAUT2. Informatics for Health and Social Care, 43(2), 109-125. DOI: 10.1080/17538157.2017.1363759 Background: The future of healthcare delivery is becoming more citizen centered, as today’s user is more active and better informed. Governmental institutions are promoting the deployment and use of online services such as Electronic Health Record (EHR) portals. This makes the adoption of EHR portals an important field to study and understand. Objective: The aim of this study is to understand the factors that drive individuals to adopt EHR portals. Methods: This study applies the extended unified theory of acceptance and usage technology (UTAUT2) to explain patients’ individual adoption of EHR portals. An online questionnaire was administered. We collected 386 valid responses. Results: The statistically significant drivers of behavioral intention are performance expectancy ((Formula presented.)=0.17; p

Tavares, J., & Oliveira, T. (2017). Electronic Health Record Portal Adoption: A cross country analysis. BMC Medical Informatics and Decision Making, 17(1), [97]. https://doi.org/10.1186/s12911-017-0482-9 Background: This study's goal is to understand the factors that drive individuals to adopt Electronic Health Record (EHR) portals and to estimate if there are differences between countries with different healthcare models. Methods: We applied a new adoption model using as a starting point the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) by incorporating the Concern for Information Privacy (CFIP) framework. To evaluate the research model we used the partial least squares (PLS) - structural equation modelling (SEM) approach. An online questionnaire was administrated in the United States (US) and Europe (Portugal). We collected 597 valid responses. Results: The statistically significant factors of behavioural intention are performance expectancy (β ^ \widehat{\beta} total = 0.285; P < 0.01), effort expectancy (β ^ \widehat{\beta} total = 0.160; P < 0.01), social influence (βtotal = 0.198; P < 0.01), hedonic motivation (β ^ \widehat{\beta} total = -0.141; P < 0.01), price value (β ^ \widehat{\beta} total = 0.152; P < 0.01), and habit (β ^ \widehat{\beta} total = 0.255; P < 0.01). The predictors of use behaviour are habit (β ^ \widehat{\beta} total = 0.145; P < 0.01), and behavioural intention (β ^ \widehat{\beta} total = 0.480; P < 0.01). Social influence, hedonic motivation, and price value are only predictors in the US group. The model explained 53% of the variance in behavioural intention and 36% of the variance in use behaviour. Conclusions: Our study identified critical factors for the adoption of EHR portals and significant differences between the countries. Confidentiality issues do not seem to influence acceptance. The EHR portals usage patterns are significantly higher in US compared to Portugal. publishersversion published

Tavares, J., & Oliveira, T. (2016). Electronic health record patient portal adoption by health care consumers: An acceptance model and survey. Journal Of Medical Internet Research, 18(3), [e49]. https://doi.org/10.2196/jmir.5069 Background: The future of health care delivery is becoming more citizen centered, as today's user is more active, better informed, and more demanding. Worldwide governments are promoting online health services, such as electronic health record (EHR) patient portals and, as a result, the deployment and use of these services. Overall, this makes the adoption of patient-accessible EHR portals an important field to study and understand. Objective: The aim of this study is to understand the factors that drive individuals to adopt EHR portals. Methods: We applied a new adoption model using, as a starting point, Ventkatesh's Unified Theory of Acceptance and Use of Technology in a consumer context (UTAUT2) by integrating a new construct specific to health care, a new moderator, and new relationships. To test the research model, we used the partial least squares (PLS) causal modelling approach. An online questionnaire was administrated. We collected 360 valid responses. Results: The statistically significant drivers of behavioral intention are performance expectancy (beta=.200; t=3.619), effort expectancy (beta=.185; t=2.907), habit (beta=.388; t=7.320), and self-perception (beta=.098; t=2.285). The predictors of use behavior are habit (beta=0.206; t=2.752) and behavioral intention (beta=0.258; t=4.036). The model explained 49.7% of the variance in behavioral intention and 26.8% of the variance in use behavior. Conclusions: Our research helps to understand the desired technology characteristics of EHR portals. By testing an information technology acceptance model, we are able to determine what is more valued by patients when it comes to deciding whether to adopt EHR portals or not. The inclusion of specific constructs and relationships related to the health care consumer area also had a significant impact on understanding the adoption of EHR portals. publishersversion published