Patient-Centered Communication Preferences in AI-Powered Mental Health Chatbots
Evidence from Two Preregistered Studies
Bibliographic Data
| ID | 19235707 |
|---|---|
| Authors | Katharina Angermayr (0009-0003-5816-426X, University of Augsburg, corresponding author), Nathalie Laura Neuendorf (0000-0002-5938-8578, University of Augsburg), Sebastian Scherr (0000-0003-4730-1575, University of Augsburg) |
| Year | 2026 |
| Pages | 1-16 |
| Publication date | 2026-06-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Health Communication (JOURNAL) |
| Journal identifiers | ISSN: 1041-0236 • E-ISSN: 1532-7027 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10410236.2026.2666885 |
| PMID | 42290401 |
| OpenAlex | W7164847817 |
| Language | EN |
| References cited | 105 |
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
Applied Choice Analysis
Best-Worst Scaling
Capturing Causal Complexity
Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum
An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being
Conversational agents in healthcare
ChatGPT Utility in Healthcare Education, Research, and Practice
How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity
Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot)
The alliance in adult psychotherapy
Improving the Spiritual Dimension of Whole Person Care
Patient-centered communication in cancer care: Promoting healing and reducing suffering
Toward a Theory of Interactive Media Effects (Time)
The effects of human-like social cues on social responses towards text-based conversational agents—a meta-analysis
Effects of social skills on lexical alignment in human-human interaction and human-computer interaction
User preferences and trust in hypothetical analog, digitalized and AI-based medical consultation scenarios
Explaining the use of AI chatbots as context alignment
Patients' valuation of the prescribing nurse in primary care
Users’ Intention to Continue Using Online Mental Health Communities
Brief Commentary
Machines and Mindlessness
| Citation velocity | historical |
|---|---|
| Highly cited | No |