User Perceptions of Virtual Consultations and Artificial Intelligence Assistance: A Mixed Methods Study

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persis-tent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed-methods study aims to better understand the virtual consulta-tion experiences and attitudes toward AI assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human-computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi structured interviews with patients (n=10), focus groups with HCPs (n=15), and an online survey (n=83) were conducted. Qualitative data was ana-lysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε² effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The mean TUQ total score of 90.6 (SD=15.0) indicates high usa-bility and user satisfaction, and there were no significant group differences (p >0.05; ε² = 0.002–0.032). There was a clear preference for hybrid models, while a lack of em-pathy was identified during remote interactions. Users were cautiously open to AI as-sistance, contingent upon transparency, human oversight, and data integrity. Views on AI assistance did not differ significantly across groups (p >0 .05; ε² = 0.005–0.065). Conclusion: Virtual consultations for chronic conditions are widely usable and ac-ceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. This study has also identified evi-dence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence based virtual care platforms, poli-cies and supportive AI tools to sustain remote chronic care delivery.