BACKGROUND: Video consultations have the potential to play a significant role for the future of healthcare by solving some of the imminently arising healthcare challenges, as pointed by the European Commission in Europe and the National Academy of Medicine in the United States of America. This technology can improve quality, efficiency, and enhance access to healthcare. OBJECTIVE: The aim of this study is to explore and understand individual video consultations acceptance drivers. METHODS: An extended technology acceptance model was created based on the diffusion of innovation theory (DOI), unified theory of acceptance and use of technology (UTAUT), health belief model (HBM), and concerns for information privacy framework (CFIP). 346 valid responses were collected through an online questionnaire, and the partial least squares (PLS) modeling approach was used to test the model. RESULTS: The model explained 77.6 % (R2) of the variance on intention to use, and 71.4 % (R2) of the variance in attitude. The predictors of intention to use are attitude (beta = 0.504, -value<0.001), performance expectancy (beta = 0.196, -value = 0.002), and COVID-19 (beta = 0.151, -value<0.001). The predictors of attitude are performance expectancy (beta = 0.643, p-value>0.001), effort expectancy (beta = 0.138, p-value = 0.001), and COVID-19 (beta = 0.170, p-value<0.001). CONCLUSIONS: This research model highlights the importance of creating extended acceptance models to capture the specificities of each technology in healthcare. The model created helps to understand the most important drivers of video consultation acceptance, highlighting the importance of the COVID-19 pandemic and perceived health risks.