title: Pain assessment and treatment disparities: a virtual human technology investigation.
abstract: Pain assessment and treatment is influenced by patient demographic characteristics and nonverbal expressions. Methodological challenges have limited the empirical investigation of these issues. The current analogue study employed an innovative research design and novel virtual human (VH) technology to investigate disparities in pain-related clinical decision-making. Fifty-four nurses viewed vignettes consisting of a video clip of the VH patient and clinical summary information describing a post-surgical context. Participants made assessment (pain intensity and unpleasantness) and treatment (non-opioid and opioid medications) decisions on computerized visual analogue scales. VH demographic cues of sex, race, and age, as well as facial expression of pain, were systematically manipulated and hypothesized to influence decision ratings. Idiographic and nomothetic statistical analyses were conducted to test these hypotheses. Idiographic results indicated that sex, race, age, and pain expression cues accounted for significant, unique variance in decision policies among many nurses. Pain expression was the most salient cue in this context. Nomothetic results indicated differences within VH cues of interest; the size and consistency of these differences varied across policy domains. This study demonstrates the application of VH technology and lens model methodology to the study of disparities in pain-related decision-making. Assessment and treatment of acute post-surgical pain often varies based on VH demographic and facial expression cues. These data contribute to the existing literature on disparities in pain practice and highlight the potential of a novel approach that may serve as a model for future investigation of these critical issues.
authors.affiliation: Department of Rehabilitation Medicine, University of Washington School of Medicine, Box 356490, Seattle, WA 98195-6490, USA. ahirsh@u.washington.edu
authors.forename: Adam T
authors.initials: AT
authors.lastname: Hirsh
authors.forename: Steven Z
authors.initials: SZ
authors.lastname: George
authors.forename: Michael E
authors.initials: ME
authors.lastname: Robinson
mesh_terms: D000328:Adult; D000700:Analgesics / Q000627:therapeutic use; D001709:Biotechnology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009726:Nurses / Q000706:statistics & numerical data; D015588:Observer Variation; D010146:Pain / Q000175:diagnosis* / Q000188:drug therapy*; D010147:Pain Measurement / Q000379:methods*; D010808:Physical Examination / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome; D014584:User-Computer Interface
chemical_list: D000700:Analgesics