Using virtual human technology to capture dentists' decision policies about pain

J Dent Res. 2013 Apr;92(4):301-5. doi: 10.1177/0022034513480802. Epub 2013 Feb 27.

Abstract

Healthcare professionals use race, gender, and age cues when making pain management decisions. Use of these demographic cues, therefore, is an important topic in the study of healthcare disparities. This study used virtual human (VH) technology to investigate the effects of VH patients' demographic cues on dentists' pain management decisions. Eighty-nine dentists viewed patients with different demographic cues. Analyses revealed that dentists rated pain intensity higher and were more willing to prescribe opioids to female, African-American, and younger patients than to their demographic counterparts. Results also found significant 2-way interactions between race and age for both pain assessment and treatment decisions. The interaction results suggest that the race difference (Caucasian < African American) was more pronounced for younger than for older patients. This is the first study to examine demographic cue use in dentists' decision-making for pain. The study found that dentists used demographic cues when making pain management decisions. Currently, there are no guidelines for decision- making practices for gender-, race-, or age-related pain. Since dentists see thousands of patients during their careers, the use of demographic cues could affect a substantial portion of the population. The findings could improve future training programs for dentists and dental students.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Analgesics / therapeutic use*
  • Attitude of Health Personnel
  • Computer Simulation
  • Decision Making
  • Dentists / psychology*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Pain / drug therapy
  • Pain / prevention & control*
  • Pain Management / statistics & numerical data*
  • Practice Patterns, Dentists' / statistics & numerical data*
  • Process Assessment, Health Care / methods
  • Racial Groups
  • Sex Factors
  • User-Computer Interface
  • Young Adult

Substances

  • Analgesics