Investigating patient characteristics on pain assessment using virtual human technology.

Pain assessment and treatment is challenging and can be influenced by patient demographic characteristics. Few research studies have been able to specifically examine these influences experimentally. The present study investigated the effects of patients' sex, race, age, and pain expression on healthcare students' assessment of pain and pain-related sequelae using virtual human (VH) technology. A lens model design was employed, which is an analogue method for capturing how individuals use environmental information to make judgments. In this study, decision-making policies were captured at the nomothetic and idiographic level. Participants included 107 healthcare students who viewed 32 VH patients that differed in sex, race, age, and pain expression in an online study. Participants provided ratings on a 100-point scale on the VH pain intensity, pain unpleasantness, negative mood, coping, and need for medical treatment. Nomothetic analyses revealed that female, African-American, older, and high pain expression VH were rated higher than male, Caucasian, younger, and low pain expression VH, respectively, on most of the five ratings. Idiographic analyses revealed detailed findings for individuals' decision-making policies. VH technology and the lens model design were shown to be highly effective in examining individuals' decision-making policies. Pain assessment often varied among individuals based on patient demographic and facial expression cues. This study could serve as a model for future investigations of pain assessment and treatment in healthcare students and providers.

Duke Scholars

Author Steven Zachary George Orthopaedic Surgery