A Bayesian Cue Integration approach to racial bias in pain assessment and treatment

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University of Delaware
Patients' reports of subjective pain experience are at least nominally a primary diagnostic cue in assessment and treatment of pain. Despite this, there is low concordance between provider assessments and self-reported pain ratings, such that patient pain is regularly underestimated and undertreated. Such discrepancies in care are particularly stark for Black patients, who receive less adequate pain care compared to White patients. While attending to facial expressions of pain marginally improves concordance in patient-provider pain ratings, it is not clear that this would improve concordance for Black patients given previous work demonstrating blunted recognition of pain on Black faces. Moreover, it is unclear how self-reported pain information is integrated with facial expressions of pain, and whether this integration is similarly biased as a function of patient race. In the present paper we construct three models of pain assessment for both Black and White targets to examine how individuals use facial expression and self-reported pain cues in making holistic judgements of pain intensity as well as subsequent treatment decisions. Overall, we find that the Bayesian Cue Integration model (compared to Face Dominant and Self-Report Dominant models) best predict participant assessments of pain as well as treatment outcomes for both Black and White targets, suggesting that both facial expression and self-report are integrated in pain assessment. ☐ Keywords: Social cognition, pain, race, cue integration
Racial bias, Bayesian cue, Pain ratings, Pain Assessment, White targets