The influence of patient race, sex, pain-related body postures, and anxiety status on pain management: a virtual human technology investigation.

The influence of patient race, sex, pain-related body postures, and anxiety status on pain management: a virtual human technology investigation.

Clark, Jaylyn;Robinson, Michael E;
journal of pain research 2019 Vol. 12 pp. 2637-2650
247
clark2019thejournal

Abstract

The purpose of this study was to examine mechanisms underlying disparities in pain management among patients with psychological comorbidities. Studies have consistently shown that health care providers, health care trainees, and laypeople are susceptible to biased assessment and treatment decisions for patients presenting with pain. Further, psychological factors may influence the use of demographic and behavioral cues in pain assessment and treatment decisions. The present study employed innovative virtual human technology to capture decision-making approaches at both the group- and individual-level to better elucidate the influence of psychological factors, demographic cues, and pain-related body postures on pain assessment and treatment decisions.One hundred and thirty-two providers and trainees in the areas of nursing, physical therapy, and medicine viewed separate, empirically validated virtual human profiles that systematically varied across pain behaviors, anxiety status, race, and sex. Participants provided pain assessment and treatment ratings using a visual analog scale for each virtual human profile.Idiographic analyses revealed that participants used patient pain-related body postures most consistently and reliably across ratings. Nomothetic analyses showed anxious virtual humans were identified as having more anxiety and more likely to be recommended anti-anxiety medications, especially by female participants.This innovative study successfully explored the influence of patient pain-related body postures, anxiety status, and demographic characteristics on pain management decisions with virtual human technology and a Lens model design. Results of this study can be used to better inform clinical practice, research, and education regarding the influence of patient variables on pain assessment and treatment decisions.

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