AMIA 2024
Implicit bias in patient-provider communication can lead to healthcare inequities, yet it is challenging to detect. Using ASR and NLP, we developed a pipeline to analyze social signals in audio recordings of 782 primary care visits, achieving 90.1% accuracy and fairness across patient groups. The analysis revealed significant disparities in provider behaviors, with more patient-centered communication observed toward white patients, highlighting the potential of automated tools to uncover biases and promote equitable healthcare.
AMIA 2023
Implicit biases in healthcare harm communication, decision-making, and care quality for marginalized patients. Through co-design workshops with 32 BIPOC, LGBTQ+, and QTBIPOC individuals, we identified four patient-centered solutions: accountability measures, real-time correction, enablement tools, and provider resources. These insights advance patient-focused approaches to addressing bias in care.