Slicing it thin: New methods for brief sampling analysis using RIAS-coded medical dialogue

Debra L. Roter, Judith A. Hall, Danielle Blanch-Hartigan, Susan Larson, Richard M. Frankel

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Objective: To explore the relationship between one-minute slices and full-session interaction and the predictive validity of the slices to ratings of affect and rapport. Methods: Third-year medical students (n=253) were videotaped during an OSCE. All interaction was coded using the Roter Interaction Analysis System (RIAS) and samples were drawn at minutes 1, 5, and 9 and extracted from the coded database. The slices were related in multivariate analysis to full-session interaction, corrected for slice content, and correlated with affect ratings of participants and independently rated judgments of rapport. Results: One-minute slices explained 33% of full-session variance in student interaction and 30% of variance in standardized patient interaction. Slices were significantly correlated with affective ratings of participants and independent judgments of rapport in a similar pattern as full-session interaction analysis. Conclusions: One-minute slices of interaction can provide a meaningful degree of insight into OSCE session communication with both concurrent and predictive validity to ratings of session affect and rapport. Practice implications: Evidence of concurrent and predictive validity further supports use of this approach as a research tool that provides an efficient means of analyzing processes of care, examining variation in communication throughout a visit and predicting visit outcomes.

Original languageEnglish (US)
Pages (from-to)410-419
Number of pages10
JournalPatient Education and Counseling
Volume82
Issue number3
DOIs
StatePublished - Mar 2011

Keywords

  • Medical students
  • Patient-provider communication
  • Rapport
  • Roter Interaction Analysis System (RIAS)
  • Standardized patients
  • Thin slice analysis

ASJC Scopus subject areas

  • General Medicine

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