Cognitive bias is pervasive in healthcare. It drives differential diagnosis and timely recognition of acute onset illness, but it also contributes to healthcare inequity. Patients may not be treated equitably due to different identities (race, gender, socio-economic status, etc) or different diseases (obesity, diabetes, hypertension, etc). In our work we investigate if biased behaviors between patients and providers can be detected through a technique known as Social Signal Processing. Our project explores how computational sensing can be used to identify behavior biases, and if it can promote improved patient-provider communication, ultimately reducing health disparities for low income, racially diverse patients in primary care. Through a partnership with academic and community-based health systems in Seattle and San Diego, we aim to characterize behavior between providers and patients, develop a behavior sensing tool, design interventional feedback, and evaluate how effective that tool and feedback are at improving patient-provider communication. We believe that this approach will lead to new techniques for shaping the next generation of healthcare providers and educators, helping them better promote healthcare access, quality, and equity.