ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2024
ConverSense: An Automated Approach to Assess Patient-Provider Interactions using Social Signals.
Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- The effect of AI-enabled virtual patient simulation on training outcomes and insecurities in psychotherapy education.BMC medical education · 2026Trial
- Envisioning the future of primary care: intervention strategies to support patient-centered communication feedback technology.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Envisioning the future of primary care: intervention strategies to support patient-centered communication feedback technology.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial intelligence-generated feedback on social signals in patient-provider communication: technical performance, feedback usability, and impact.JAMIA open · 2024Article
- Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Implementing artificial intelligence (AI)-supported communication tools in healthcare: System-level perspectives.Digital healthArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
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. This pipeline is embedded into ConverSense, a web-application for providers to visualize their communication patterns, both within and across visits. Our user study with 5 clinicians and 10 patient visits demonstrates ConverSense's potential to provide feedback on communication challenges, as well as the need for this feedback to be contextualized within the specific underlying visit and patient interaction. Through this novel approach that uses data-driven self-reflection, ConverSense can help providers improve their communication with patients to deliver improved quality of care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.