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Electronic Health Record Patient Portal Adoption by Health Care Consumers: An Acceptance Model and Survey

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 a...

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Detalles Bibliográficos
Autores principales: Tavares, Jorge, Oliveira, Tiago
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795321/
https://www.ncbi.nlm.nih.gov/pubmed/26935646
http://dx.doi.org/10.2196/jmir.5069
Descripción
Sumario: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.