The influences of media naturalness and mental model alignment on reducing patient uncertainty in virtual consultation

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Abstract

Virtual consultation (VC) can be simply referred to as a telemedicine service that enables patients to access doctors remotely. Many patients still have uncertainties regarding the consultation process and results, which mostly concern communication with doctors. This motivated us to explore the answers to the following question: How do media naturalness and mental model alignment influence patients’ uncertainties in virtual consultation and how do these uncertainties affect patient satisfaction towards the consultation? A two (naturalness: low vs. high) by two (mental model alignment: low vs. high) field experiment was conducted to answer this question. For each group, participants were asked to answer corresponding questions regarding their uncertainties and their satisfaction towards the consultation. 327 valid questionnaire was obtained at the end. The results show that during patient-doctor communication, more natural the VC system is, the less uncertainties patients will have during their communication with doctors. Meanwhile, higher level of mental model alignment the VC system has, the less uncertainties patients will have during the communication. The results suggest that healthcare providers should select systems which are more natural to patients, and design them to align with patients’ mental models of visiting doctors to reduce patients’ uncertainties. The results also show that higher level of uncertainty leads to lower level of patient satisfaction towards the consultation. The findings of this study will redound to the benefit of academic society in virtual consultation considering that naturalness and mental model alignment play important roles in reducing patients’ uncertainties during their communication to doctors.

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APA

Shi, Y. V., Komiak, S., & Komiak, P. (2020). The influences of media naturalness and mental model alignment on reducing patient uncertainty in virtual consultation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12183 LNCS, pp. 147–164). Springer. https://doi.org/10.1007/978-3-030-49065-2_11

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