Investigating patient characteristics on pain assessment using virtual human technology

Abstract

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.

Using Virtual Human Technology to Capture Dentists’ Decision Policies about Pain

“…The current study addressed this gap in the literature; we used VH technology to examine the influence of patients' gender, race, and age on dentists' pain-related decision-making. Overall, the results of group-level analyses were consistent with those reported in previous studies on non-dental pain (Hirsh et al, 2009a,b;Stutts et al, 2010). As expected, dentists rated pain intensity higher and were more willing to administer opioid analgesics to high-pain-expressing faces than to lowpain-expressing faces, indicating that pain level is a salient cue in their decisions about pain and that the VH stimuli validly represented pain level.…”

“…This approach utilizes virtual human (VH) technology that has high visual fidelity and expressive facial animation (Hirsh et al, 2009a,b;Stutts et al, 2010;Wandner et al, 2010). Since the patient is not present, there is a greater likelihood that a participant will report his/her perceptions and treatment opinions with less social desirability bias (Chang and Krosnick, 2010).…”

Defining Gender Disparities in Pain Management

“…Most of the research we have in this area has used standardized patient vignettes or simulations to assess providers' beliefs and proposed behaviors (eg, whether to prescribe an opioid analgesic) while controlling for all possible patient factors other than sex. Recent research using ''virtual human'' technology suggests females are judged by US healthcare students as having more pain than males, even after controlling for race, age, and facial expressions of pain [46]. In this study, the patient's gender accounted for 12% of the variance in judged pain intensity and 16% of the variance in judged pain unpleasantness.…”

Pain Assessment and Treatment Decisions for Virtual Human Patients

“…[13][14][15][16] There are a number of pain behavior assessment methodologies (direct observation of patients' pain behaviors via continuous observation, duration recording, frequency recording or interval recording, the Pain Behavior Observation System, actigraphy, etc.) that assess stereotypical nonverbal, and nonfacial expressions of pain.…”

“…In previous VH research, investigators found that undergraduates, healthcare trainees, and healthcare professionals use gender and race, at least in part, to make pain assessment and treatment decisions. [13][14][15][16] In these previous studies, participants assessed pain via VH facial expressions that have high visual and expressive fidelity in expressing high and low pain. Participants also read a scenario in which the VH-patients had been experiencing chronic back pain for more than a year.…”