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Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations

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.

Enhancing Accuracy, Time Spent, and Ubiquity in Critical Healthcare Delineation via Cross-Device Contouring

DIS 2024

ConverSense: An Automated Approach to Assess Patient-Provider Interactions using Social Signals

CHI 2024 Analyzing patient-provider communication through social signals like dominance, interactivity, engagement, and warmth can help improve care by identifying opportunities for better interactions. We introduce a machine-learning pipeline embedded in ConverSense, a web application that visualizes communication patterns across visits. A user study with clinicians and patients highlights its potential to provide actionable, context-specific feedback for enhancing communication quality and patient outcomes.

Designing Communication Feedback Systems To Reduce Healthcare Providers’ Implicit Biases In Patient Encounters

CHI 2024 Implicit bias among healthcare providers can negatively impact care quality and patient outcomes, necessitating tools to identify and address these biases. Through design sessions with 24 primary care providers, we found they prefer feedback with transparent metrics, trends across visits, and actionable tips presented in a dashboard. These insights can guide the development of interactive systems to support equitable healthcare, especially for marginalized communities.

“I’d be watching him contour till 10 o’clock at night”: Understanding Tensions between Teaching Methods and Learning Needs in Healthcare Apprenticeship

CHI 2024

PaperToPlace - Transforming Instruction Documents into Spatialized and Context-Aware Mixed Reality Experiences

UIST 2023 Paper instructions are a mainstream medium for sharing knowledge. However, consuming such instructions and translating them into activities can be inefficient due to the lack of connectivity with the physical environment. PaperToPlace is a novel workflow comprising an authoring pipeline, which allows the authors to rapidly transform and spatialize existing paper instructions into an MR experience, and a consumption pipeline, which computationally places each instruction step at an optimal location that is easy to read and does not occlude key interaction areas. This is a collaborative project with Adobe Research.

Embodied Exploration - Facilitating Remote Accessibility Assessment for Wheelchair Users with Virtual Reality

ASSETS 2023 Assessing accessibility for wheelchair users can be challenging, due to lack of accessibility details needed for individual users. Embodied Exploration is a VR technique to deliver the experience of a physical visit while keeping the convenience of remote assessment. Embodied Exploration allows wheelchair users to explore high-fidelity digital replicas of physical environments with themselves embodied by avatars, leveraging the increasingly affordable VR headsets.

Screen or No Screen? Lessons Learnt from a Real-World Deployment Study of Using Voice Assistants With and Without Touchscreen for Older Adults

ASSETS 2023

How do Older Adults Set Up Voice Assistants? Lessons Learned from a Deployment Experience for Older Adults to Set Up Standalone Voice Assistants

DIS 2023

Design and Development of a Training and Immediate Feedback Tool to Support Healthcare Apprenticeship

CHI 2023