# Applying Novel Analytic Methods to Address the Impact of Race on Patient-Provider Communication

> **NIH VA I21** · PORTLAND VA MEDICAL CENTER · 2021 · —

## Abstract

Background: Evidence from VA and non-VA settings demonstrates widespread racial disparities in
healthcare delivery. Our prior work suggests that providers with higher levels of “cultural competence” (CC)
deliver more equitable care. But how CC translates into better care is unclear. We will use data from our
current project, “Opening the Black Box of Cultural Competence” (aka Black Box), in which we are analyzing
communication content from audio recorded primary care visits. We will complement these content analyses
with computerized linguistic analysis methods to generate and test novel measures of patient-provider
communication and examine their role in disparities in patient-provider relationship quality.
Significance/Impact: Delivering high-quality care to all Veterans is central to VA's mission. We will address
a previously unexplored area that represents a potential target for reducing disparities in VA care. Our study
also addresses several VA HSR&D priority areas, including promoting health equity, improving primary care
practice, and advancing data science.
Innovation: Most studies of healthcare communication have focused on communication content (i.e., what is
said). By examining linguistic style (i.e., how things are said), we will address a relatively unexplored potential
source of racial disparities. In addition, by applying computerized, natural language processing (NLP) methods
to evaluate patient-provider communication, this study will develop and test potentially scalable tools and
metrics that can be implemented to provide real-time feedback to improve patient-provider interactions as part
of a learning health system striving to improve the delivery of high-quality, equitable care.
Specific Aims:
1) Apply computerized text analysis tools to transcripts of primary care visits to generate measures of patient
 and provider linguistic style and style matching (LSM).
2) Test associations of: a) Veteran race and provider CC with LSM and provider linguistic style; and b) LSM
 and provider linguistic style with the quality of patient-provider relationships.
3) Qualitatively explore examples of visits with high and low LSM and with provider linguistic style patterns
 associated with high and low relationship quality.
Methodology: In the Black Box study, we are analyzing communication content, using the Roter Interaction
Analysis System, directly from the audio files of 408 primary care visits at 4 geographically diverse VA medical
centers. In the proposed project, we will transcribe the audio files and apply computerized, dictionary-based
lexical analysis tools to evaluate functional and semantic speech patterns and LSM between patient and
provider. We will test the associations described in Aim 2 using patient and provider survey data collected in
the parent study. Finally, we will qualitatively review selected transcripts to evaluate the mechanisms by which
LSM, and provider linguistic styles associated with relationship quality, are a...

## Key facts

- **NIH application ID:** 10187911
- **Project number:** 1I21HX003376-01
- **Recipient organization:** PORTLAND VA MEDICAL CENTER
- **Principal Investigator:** SOMNATH SAHA
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2021-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10187911

## Citation

> US National Institutes of Health, RePORTER application 10187911, Applying Novel Analytic Methods to Address the Impact of Race on Patient-Provider Communication (1I21HX003376-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10187911. Licensed CC0.

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