Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations
This addresses the problem of healthcare inequities by enabling automated detection of biased communication signals, though it is incremental as it applies existing methods to a new domain.
The study tackled the problem of detecting implicit bias in patient-provider interactions by developing an automated pipeline using ASR and NLP to analyze social signals from audio recordings of 782 primary care visits, achieving 90.1% average accuracy and identifying statistically significant differences in provider behavior favoring white patients.
Implicit bias can impede patient-provider interactions and lead to inequities in care. Raising awareness is key to reducing such bias, but its manifestations in the social dynamics of patient-provider communication are difficult to detect. In this study, we used automated speech recognition (ASR) and natural language processing (NLP) to identify social signals in patient-provider interactions. We built an automated pipeline to predict social signals from audio recordings of 782 primary care visits that achieved 90.1% average accuracy across codes, and exhibited fairness in its predictions for white and non-white patients. Applying this pipeline, we identified statistically significant differences in provider communication behavior toward white versus non-white patients. In particular, providers expressed more patient-centered behaviors towards white patients including more warmth, engagement, and attentiveness. Our study underscores the potential of automated tools in identifying subtle communication signals that may be linked with bias and impact healthcare quality and equity.