Patient-Centered Communication Preferences in AI-Powered Mental Health Chatbots

Evidence from Two Preregistered Studies

Datos Bibliográficos

ID19235707
AutoresKatharina Angermayr (0009-0003-5816-426X, University of Augsburg, autor de correspondencia), Nathalie Laura Neuendorf (0000-0002-5938-8578, University of Augsburg), Sebastian Scherr (0000-0003-4730-1575, University of Augsburg)
Año2026
Páginas1-16
Fecha de publicación2026-06-15
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaHealth Communication (JOURNAL)
Identificadores de la revistaISSN: 1041-0236 • E-ISSN: 1532-7027
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10410236.2026.2666885
PMID42290401
OpenAlexW7164847817
IdiomaEN
Referencias citadas105

Access to mental health information is shifting from static search to conversational AI. Guided by patient-centered communication (PCC), two preregistered U.S. studies identified preferred communication features for interactions with AI chatbots about mental health and how individuals trade them off within feature bundles. Study 1 (N = 414, US quota sample) used a Best–Worst Scaling (BWS) to identify the six most relevant PCC-aligned features for healthcare providers. Study 2 analyzed an AI chatbot subsample (n = 268) drawn from a U.S. quota-representative sample in a Discrete Choice Experiment (DCE) to quantify trade-offs between combinations of these preferred features. Across both studies, users strongly wanted two communication features simultaneously in AI mental-health chatbots: reflective listening and multi-symptom assessment. Importantly, relational and clinical PCC-aligned features are most highly valued in interactions with AI mental-health chatbots. These preferences remained largely consistent across users and their preferences for communication accommodation

Active listening · Chatbot · Health communication · Information seeking · Mental health · Self-disclosure · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Digital Mental Health Interventions