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

Objectives: Implicit bias perpetuates health care inequities and manifests in patient-provider interactions, particularly nonverbal social cues like dominance. We investigated the use of artificial intelligence (AI) for automated communication assessment and feedback during primary care visits to raise clinician awareness of bias in patient interactions.

Materials And Methods: (1) Assessed the technical performance of our AI models by building a machine-learning pipeline that automatically detects social signals in patient-provider interactions from 145 primary care visits.

Guardian Reasons for Accessing Their Transgender and Gender-Diverse Adolescent's Patient Portal Account.

Purpose: To understand if and why guardians access their adolescent child's electronic health record patient portal account.

Methods: Guardians of transgender and gender-diverse adolescents completed a survey regarding patient portal use. Descriptive statistics were used to describe items related to guardian access to adolescent portal accounts.

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

Healthcare providers' implicit bias, based on patients' physical characteristics and perceived identities, negatively impacts healthcare access, care quality, and outcomes. Feedback tools are needed to help providers identify and learn from their biases. To incorporate providers' perspectives on the most effective ways to present such feedback, we conducted semi-structured design critique sessions with 24 primary care providers.

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

Patient-provider communication influences patient health outcomes, and analyzing such communication could help providers identify opportunities for improvement, leading to better care. Interpersonal communication can be assessed through "social-signals" expressed in non-verbal, vocal behaviors like interruptions, turn-taking, and pitch. To automate this assessment, we introduce a machine-learning pipeline that ingests audio-streams of conversations and tracks the magnitude of four social-signals: dominance, interactivity, engagement, and warmth.

Desire for Gender-Affirming Medical Care Before Age 18 in Transgender and Nonbinary Young Adults.

We aimed to understand transgender and nonbinary (TNB) young adults' desire to receive gender-affirming medical care (GAMC) before age 18 and identify barriers and facilitators to receiving this care in adolescence. A cross-sectional survey was administered to TNB young adults presenting for care between ages 18 and 20 in 2023. Descriptive statistics characterized the sample, χ tests with pairwise comparisons identified differences in desire for gender-affirming medications, outness, and parental consent by gender identity and sex assigned at birth, and -tests evaluated differences in barriers and facilitators to receiving care by outness to parents.