Objectives To elicit stated preferences and willingness-to-pay (WTP) for artificial intelligence (AI)-enabled blended care in type 2 diabetes mellitus (T2DM), and to examine preference heterogeneity by digital experience and socioeconomic status (SES). Design Cross-sectional discrete choice experiment (DCE).
Setting 12 community health centres in Jiaozuo and Puyang, Henan Province, China. Data were collected between June and August 2025.
Participants 423 adults diagnosed with T2DM for at least 6 months, recruited using consecutive convenience sampling from routine follow-up appointments. Of 769 participants who completed the survey, 346 were excluded following prespecified data quality criteria (retention rate: 55.0%).
Outcome measures Outcome measures included preference weights and WTP (in Chinese Yuan, ¥) for five DCE attributes: monthly subscription fee, recommendation source, feedback modality, in-person follow-up frequency and expert oversight, estimated using mixed logit models. Simulated uptake probabilities for tailored service packages across four user profiles were computed.
Results Among 423 participants, 80.6% had never used AI tools. Price was the dominant driver of choice (62.7% relative attribute importance).
BMJ Open published a clinical update in Research Highlights on 20 May 2026.
The item focuses on Patient preferences and willingness-to-pay for AI-enabled blended type 2 diabetes care by digital experience and socioeconomic status: a discrete-choice experiment in China.
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