Patient-centered communication preferences in AI-powered mental health chatbots: evidence from two preregistered studies

  • 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 preferencesAccess 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.show moreshow less

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Metadaten
Author:Katharina AngermayrORCiDGND, Nathalie Laura NeuendorfORCiDGND, Sebastian ScherrORCiDGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/131345
ISSN:1041-0236OPAC
ISSN:1532-7027OPAC
Parent Title (English):Health Communication
Publisher:Informa UK
Place of publication:London
Type:Article
Language:English
Year of first Publication:2026
Publishing Institution:Universität Augsburg
Release Date:2026/06/26
DOI:https://doi.org/10.1080/10410236.2026.2666885
Institutes:Philosophisch-Sozialwissenschaftliche Fakultät
Fakultätsübergreifende Institute und Einrichtungen
Philosophisch-Sozialwissenschaftliche Fakultät / imwk - Institut für Medien, Wissen und Kommunikation
Fakultätsübergreifende Institute und Einrichtungen / Zentrum für Interdisziplinäre Gesundheitsforschung (ZIG)
Philosophisch-Sozialwissenschaftliche Fakultät / imwk - Institut für Medien, Wissen und Kommunikation / Lehrstuhl für Digital Health Communication
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Latest Publications (not yet published in print):Aktuelle Publikationen (noch nicht gedruckt erschienen)
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung