Artificial intelligence-generated feedback on social signals in patient-provider communication: technical performance, feedback usability, and impact.

ABSTRACT:

Objectives

Implicit bias perpetuates health care inequities and manifests in patient-provider interactions, particularly nonverbal social cues like dominance. We investigated the use of artificial intelligence (AI) for automated communication assessment and feedback during primary care visits to raise clinician awareness of bias in patient interactions.

Materials and methods

(1) Assessed the technical performance of our AI models by building a machine-learning pipeline that automatically detects social signals in patient-provider interactions from 145 primary care visits. (2) Engaged 24 clinicians to design usable AI-generated communication feedback for their workflow. (3) Evaluated the impact of our AI-based approach in a prospective cohort of 108 primary care visits.

Re

SUBMITTER: Bedmutha MS 

PROVIDER: S-EPMC11488971 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature