The doctor’s digital double: how warmth, competence, and animation promote adherence intention

Date
2018-11
Language
English
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Abstract

Background Each year, patient nonadherence to treatment advice costs the US healthcare system more than $300 billion and results in 250,000 deaths. Developing virtual consultations to promote adherence could improve public health while cutting healthcare costs and usage. However, inconsistencies in the realism of computer-animated humans may cause them to appear eerie, a phenomenon termed the uncanny valley. Eeriness could reduce a virtual doctor’s credibility and patients’ adherence.

Methods In a 2 × 2 × 2 between-groups posttest-only experiment, 738 participants played the role of a patient in a hypothetical virtual consultation with a doctor. The consultation varied in the doctor’s Character (good or poor bedside manner), Outcome (received a fellowship or sued for malpractice), and Depiction (a recorded video of a real human actor or of his 3D computer-animated double). Character, Outcome, and Depiction were designed to manipulate the doctor’s level of warmth, competence, and realism, respectively.

Results Warmth and competence increased adherence intention and consultation enjoyment, but realism did not. On the contrary, the computer-animated doctor increased adherence intention and consultation enjoyment significantly more than the doctor portrayed by a human actor. We propose that enjoyment of the animated consultation caused the doctor to appear warmer and more real, compensating for his realism inconsistency. Expressed as a path model, this explanation fit the data.

Discussion The acceptance and effectiveness of the animation should encourage the development of virtual consultations, which have advantages over creating content with human actors including ease of scenario revision, internationalization, localization, personalization, and web distribution.

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Dai, Z., & MacDorman, K. F. (2018). The doctor's digital double: how warmth, competence, and animation promote adherence intention. PeerJ Computer Science, 4(e168), 1–29. https://doi.org/10.7717/peerj-cs.168
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