# UnBIASED: Understanding Biased patient-provider Interaction And Supporting Enhanced Discourse

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2020 · $564,728

## Abstract

PROJECT SUMMARY/ABSTRACT
This project addresses health disparities by investigating a novel computational approach that makes implicit,
thus hidden, bias visible. Healthcare bias, based on patients’ race, gender, socioeconomic status, sexual
orientation, and other characteristics lead to health disparities. Such biases are often unintentional and hidden
in communication among clinicians and patients. Although there is broad agreement that healthcare biases need
to be better understood, assessed and mitigated, traditional clinical communication training and assessment is
removed from actual patient-provider interactions in which bias hides. To mitigate health disparities, we propose
social signal processing (SSP) technology that automatically assesses hidden bias during patient encounters.
SSP involves machine analysis and feedback on subtle cues (e.g., talk time, interruptions, body movement) that
reflect the quality of communication. This technology will automatically capture nonverbal, linguistic, and
affective, cues in patient-provider interactions and then provide feedback for improvement, designed in
collaboration with patients and providers. Guided by human-centered design, we will engage low income, racially
diverse patients and their providers to inform the design of visual feedback from SSP assessment, and then
evaluate the efficacy of this novel technology in both simulated and real world encounters. Leveraging our
preliminary work and multidisciplinary expertise from our two investigative sites, University of Washington and
University of California San Diego, we will partner with academic and community health clinics at both sites to
engage underserved patients and providers in three specific aims to: build an SSP model that characterizes
communication quality among clinicians and health disparity patients (Aim 1), design SSP feedback that conveys
hidden bias to patients and providers (Aim 2), and evaluate the efficacy of SSP technology in controlled and real
world clinical settings (Aim 3). Findings will bring insight into social signals associated with hidden bias that we
can automatically detect during patient visits, design recommendations for effective SSP feedback for both
providers and patients, and evidence on the technical validity and efficacy of SSP technology for improving
patient and provider experience of patient-centered care. To mitigate health disparities, patients and providers
need unbiased interactions. This project will contribute a novel computational paradigm using SSP that brings
human-centered visibility to implicit biases that manifest in healthcare communication and lead to health
disparities. Findings will advance biomedical informatics and health disparities research with a novel SSP
approach for the next generation of healthcare providers and educators, empower health disparity patients, and
promote healthcare quality and equity. Bringing visibility to hidden bias though human-centered SSP has
significant ...

## Key facts

- **NIH application ID:** 10021722
- **Project number:** 5R01LM013301-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Andrea L. Hartzler
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $564,728
- **Award type:** 5
- **Project period:** 2019-09-20 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021722

## Citation

> US National Institutes of Health, RePORTER application 10021722, UnBIASED: Understanding Biased patient-provider Interaction And Supporting Enhanced Discourse (5R01LM013301-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10021722. Licensed CC0.

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