UnBIASED: Understanding Biased patient-provider Interactions And Supporting Enhanced Discourse

Thu, 23 Sep 2021 00:00:00 +0000

Overview

Healthcare bias, based on patients’ race, gender, sexual orientation, and other factors, leads to health disparities, such as lack of appropriate treatment and inadequate pain support. Such biases are often unintentional and “hidden” in communication between patients and doctors.

Existing approaches to address hidden bias are limited because they are removed from actual patient-doctor interactions in which bias hides. Technology offers an opportunity to design new approaches that can make den bias more visible and thus addressable.

We are investigating a new approach to address hidden healthcare bias by improving patient-doctor communication in primary care. This approach monitors body language for signs of bias and provides feedback to raise awareness of patients and doctors for opportunities to adjust their communication style.

We are partnering closely with patients and doctors to ensure this approach is guided by their experiences and needs. Through this collaborative effort, we expect to gain a deep understanding of how hidden bias is experienced and how we can address it better in the future.

More Info here: http://unbiased.health

ConverSense

Measurement is only useful if a provider can see it. ConverSense turns the pipeline’s output into feedback: a web application that visualizes how dominance, interactivity, engagement and warmth moved through a visit, and across visits over time. A study with five clinicians across ten patient visits showed the feedback lands only when it is tied to the specific interaction it came from, which is why the visualizations are time-aligned to the conversation rather than reported as scores.

SocialLM

Assessing communication at scale is the bottleneck. SocialLM asks whether large language models can track social behaviors directly from clinical transcripts without fine-tuning, and finds that they can, but unevenly: performance varies by patient race and by segment of the visit. Because that variability is itself an equity problem, the work introduces an agreement-weighted ensemble that improves both accuracy and stability, giving a practical route to social signal tracking at scale.

Funding and External Collaborations

UnBIASED ia a 5-year project, funded by the National Library of Medicine (NLMR01LM013301), and it is a collaboration between the University of Washington and the HXI Lab at UC San Diego. Our ultimate goal is to create tools to support patients and the next generation of doctors to have bias-free interactions that promote healthcare access, quality, and equity.

Artificial intelligence-generated feedback on social signals in patient--provider communication: technical performance, feedback usability, and impact

Fri, 01 Nov 2024 00:00:00 +0000

Artificial intelligence-generated feedback on social signals in patient--provider communication: technical performance, feedback usability, and impact

Mon, 01 Jan 2024 00:00:00 +0000

Alexandra Andreiu | HXI - Human-centered eXtended Intelligence

UnBIASED: Understanding Biased patient-provider Interactions And Supporting Enhanced Discourse

Overview

ConverSense

SocialLM

Funding and External Collaborations

Artificial intelligence-generated feedback on social signals in patient--provider communication: technical performance, feedback usability, and impact

Artificial intelligence-generated feedback on social signals in patient--provider communication: technical performance, feedback usability, and impact