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: see text]=0.17; p < 0.01), effort expectancy ([Formula: see text]=0.17; p < 0.01), social influence ([Formula: see text]=0.10; p < 0.05), and habit ([Formula: see text]=0.37; p < 0.001). Habit ([Formula: see text]=0.28; p < 0.001) and behavioral intention ([Formula: see text]=0.24; p < 0.001) are the statistically significant drivers of technology use. The model explains 52% of the variance in behavioral intention and 31% of the variance in technology use. CONCLUSIONS: 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.
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: see text]=0.17; p < 0.01), effort expectancy ([Formula: see text]=0.17; p < 0.01), social influence ([Formula: see text]=0.10; p < 0.05), and habit ([Formula: see text]=0.37; p < 0.001). Habit ([Formula: see text]=0.28; p < 0.001) and behavioral intention ([Formula: see text]=0.24; p < 0.001) are the statistically significant drivers of technology use. The model explains 52% of the variance in behavioral intention and 31% of the variance in technology use. CONCLUSIONS: 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.
Entities:
Keywords:
UTAUT2; e-health; electronic health records; healthcare consumers; technology adoption