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

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported for primary outcomes

• Effect Size: Price dominance: 62.7% relative attribute importance; Rural residents WTP ¥17.58 for physician endorsement; Female participants WTP ¥18.45 for guideline recommendations

• Primary Outcome: Preference weights and willingness-to-pay for AI-enabled diabetes care attributes

• Nnt Or Sensitivity: Targeted service bundles increased uptake by 8-16 percentage points compared to non-targeted approaches

• Confidence Interval: Rural physician endorsement WTP: 95% CI ¥5.98 to ¥29.17; Female guideline WTP: 95% CI ¥7.81 to ¥29.40

Clinical Application

Requires significant healthcare system adaptation and technology infrastructure investment Limited direct applicability due to different healthcare funding models (PBS vs fee-for-service), cultural attitudes toward AI, and rural healthcare delivery systems. Would require validation in Australian diabetes populations Adults with type 2 diabetes considering digital health interventions, particularly in community healthcare settings

Abstract

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). Profound preference heterogeneity emerged across subgroups: rural residents (n=78) were highly price-sensitive but preferred physician endorsement (WTP ¥17.58 (US$2.55), 95% CI ¥5.98 to ¥29.17); female participants (n=224) valued guideline recommendations (WTP ¥18.45 (US$2.67), 95% CI ¥7.81 to ¥29.40); and diabetes app users (n=34) were the least price-sensitive but showed a negative preference for AI instant feedback, instead preferring human dietitian feedback. Expert oversight carried a consistent negative WTP across all profiles. Targeting tailored service bundles to intended subgroups increased uptake by 8-16 percentage points compared with non-targeted bundles. CONCLUSIONS: A 'digital experience paradox' exists whereby digitally experienced users view human interaction as a premium service, while underserved groups rely on specific trust markers such as physician endorsement. To avoid widening the digital divide, AI-enabled blended diabetes care must move beyond standardised models towards configurable, equity-driven service pathways.

References

1. 1.Sun, H., Shi, Z., Xia, Y., Bai, Y., Yu, M., Liu, R., & He, P. (2026). 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. BMJ Open. https://doi.org/10.1136/bmjopen-2025-115755