Virtual human technology: patient demographics and healthcare training factors in pain observation and treatment recommendations

Abstract

BackgroundPatients’ 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.DesignTo 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.ResultsResults 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.ConclusionsThis 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.

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.…”

“…18 However, results of this study differ from previous work on gender role theories of pain and facial expressions of pain, where women were generally perceived as having more pain. [13][14][15][16] It is possible that providing a behavioral display of pain via body posture and impaired movement may work in conjunction with these stereotypes to alter pain ratings, and may serve to counter stereotypical assessments. Previous clinical research suggests that healthcare providers perceive men as less likely to report experiencing pain, or displaying pain behaviors, unless they are experiencing high levels 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.…”

Using Virtual Human Technology to Provide Immediate Feedback About Participants′ Use of Demographic Cues and Knowledge of Their Cue Use

“…1,8,9,18,19 Limited focus, however, has been placed on whether individuals are aware of their use of cues, or how these individuals would respond to feedback about their potential use of cues in making judgments about another person’s pain. The current study used an experimental approach, VH technology, and LENS model analyses, to measure participants’ use of demographic cues in pain assessment and treatment.…”

“…8,16,18 The advantage of using VH technology and VH patients to evaluate pain management decisions is that it is a standardized, experimentally-controlled approach that assesses whether an individual uses demographic cues in their pain decision-making. Another advantage of VH technology is that the participants can be provided immediate feedback on their pain management decisions.…”