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Virtual Human Technology: Patient Demographics and Healthcare Training Factors in Pain Observation and Treatment Recommendations

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Virtual Human Technology: Patient Demographics and Healthcare Training Factors in Pain Observation and Treatment Recommendations

Laura D Wandner et al. J Pain Res.

Abstract

Background: Patients' sex, race, and age have been found to affect others' perception of their pain. However, the influence of these characteristics on treatment recommendations from laypersons and healthcare providers is understudied.

Design: To address this issue, 75 undergraduates and 107 healthcare trainees (HTs) used a web-based delivery system to view video clips of virtual human (VH) patients presenting with different standardized levels of pain. Subjects then rated the VHs' pain intensity and recommended the amount of medical treatment the VHs should receive.

Results: Results indicated that, compared with undergraduates, HTs perceived African Americans and older adults as having less pain but were more willing to recommend medical treatment for these patients than were undergraduate participants. HTs and undergraduates rated female, African American, older, and high-pain-expressing adults as having greater pain intensity than male, Caucasian, younger, and lower-pain-expressing adults. Moreover, they also recommended that female, older, and high-pain-expressing adults receive more medical treatment than male, younger, and lower-pain-expressing adults.

Conclusions: This study found that the characteristics of the VHs and whether the participants were undergraduates or HTs influenced the ratings of pain assessment and treatment recommendations. The findings are consistent with the previous VH literature showing that VH characteristics are important cues in the perception and treatment of pain. However, this is the first study to identify differences in pain-related decisions between individuals who are pursuing healthcare careers and those who are not. Finally, not only does this study serve as further evidence for the validity and potential of VH technology but also it confirms prior research that has shown that biases regarding patient sex, race, and age can affect pain assessment and treatment.

Keywords: age; gender; pain; perception; race; virtual human.

Figures

Figure 1
Figure 1
A young, Caucasian, female virtual human showing a high pain expression.

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