title: Evaluation of nurses' self-insight into their pain assessment and treatment decisions.
abstract: Research generally indicates that providers demonstrate modest insight into their clinical decision processes. In a previous study utilizing virtual human (VH) technology, we found that patient demographic characteristics and facial expressions of pain were statistically significant predictors of many nurses' pain-related decisions. The current study examined the correspondence between the statistically identified and self-reported influences of contextual information on pain-related decisions. Fifty-four nurses viewed vignettes containing a video of a VH patient and text describing a postsurgical context. VH sex, race, age, and facial expression varied across vignettes. Participants made pain-assessment and treatment decisions on visual analogue scales. Participants subsequently indicated the information they relied on when making decisions. None of the participants reported using VH sex, race, or age in their decision process. Statistical modeling indicated that 28 to 54% of participants (depending on the decision) used VH demographic cues. 76% of participants demonstrated concordance between their reported and actual use of the VH facial expression cue. Vital signs, text-based clinical summary, and VH movement were also reported as influential factors. These data suggest that biases may be prominent in practitioner decision-making about pain, but that providers have minimal awareness of and/or a lack of willingness to acknowledge this bias.
  
  
  CONCLUSIONS
  The current study highlights the complexity of provider decision-making about pain management. The VH technology could be used in future research and education applications aimed at improving the care of all persons in pain.
authors.affiliation: Department of Rehabilitation Medicine, University of Washington School of Medicine, Seattle, Washington, USA. ahirsh@u.washington.edu
authors.forename: Adam T
authors.initials: AT
authors.lastname: Hirsh
authors.forename: Mark P
authors.initials: MP
authors.lastname: Jensen
authors.forename: Michael E
authors.initials: ME
authors.lastname: Robinson
mesh_terms: D000328:Adult; D000367:Age Factors; D003198:Computer Simulation; D003463:Cues; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009068:Movement; D009726:Nurses / Q000523:psychology*; D010146:Pain / Q000175:diagnosis*; D059408:Pain Management; D010147:Pain Measurement; D044469:Racial Groups; D012647:Self-Assessment; D012737:Sex Factors; D014584:User-Computer Interface