AMIA 2024Using 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 2024Implicit bias impacts the quality of patient-clinician interactions, influencing patient outcomes and trust in healthcare. Most interventions to mitigate bias rely solely on expensive human assessments, rather than leveraging AI technology with clinician input.
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.