The research study examines consumers’/patients’ hesitance toward adopting metaverse-based healthcare applications. Innovation resistance theory has been taken as a basis to form hypotheses, and online survey responses were collected across India to investigate how usage barriers, value barriers, risk barriers, tradition barriers, and image barriers affect non-adoption intentions. Structural equation modeling using SmartPLS was used to analyze the primary data collected across pan India from 389 consumers/patients/medical practitioners. The results obtained indicate that risk, tradition, and image barriers significantly influence non-adoption intentions. Our findings also reveal that distrust plays a crucial role in the relationship between risk, tradition, and image barriers with non-adoption intentions. However, the usage barrier and value barrier were not significant toward distrust and non-adoption. The research highlights that psychological barriers were significant, whereas functional barriers were insignificant for non-adoption. Our insights backs to the metaverse literature by allowing a deeper awareness of metaverse adoption issues in the medical field using empirical data. The insights from the study help practitioners, healthcare business professionals, and patients to understand the concerns and misunderstandings surrounding the technology and its integration into the medical sector.