The purpose of this study is to investigate patient motivation for adopting Conversational Artificial Intelligence Health Assistants (CAIHAs) using a modified Extended Valence Framework (EVF) that includes trust, risk, benefit, and intention. Additionally, three antecedents of trust and risk are included – relative advantage, resistance to change, and privacy concerns. Partial Least Squares (PLS) was used to analyze the model with 112 participants. Results indicate a clear path to the intention to adopt CAIHAs through trust and benefit, explaining 74.3% of intention. In addition, the antecedents relative advantage and resistance to change had significant relationships with trust, while resistance to change and privacy concerns had significant relationships with risk. Overall, this research finds the path to adoption is through trust and benefit with the significant antecedents relative advantage and resistance to change.