Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 10, 2025
Date Accepted: Dec 21, 2025
Behavioral Dynamics of AI Trust and Healthcare Delays Among Adults: An Integrated Cross-Sectional Survey and Agent-Based Modeling Study
ABSTRACT
Background:
Excessive trust in healthcare artificial intelligence (AI) can unintentionally lead to delays in seeking medical care, particularly among patients with chronic illnesses. However, the behavioral dynamics underlying this phenomenon are not well understood.
Objective:
This study aims to quantify the influence of AI trust on healthcare delays through integrated survey-based mediation analysis and real-world research, and to simulate intervention efficacy using multi-agent modeling.
Methods:
A cross-sectional survey involving 2,460 Chinese adults assessed AI trust (using a 5-point Likert scale), frequency of AI usage, chronic disease status, and care delay outcomes. Mediation analysis was employed to examine the role of usage frequency as a mediator between AI trust and healthcare delays. Multi-agent simulations were conducted to model 14-day trust-delay feedback loops within small-world networks, evaluating three intervention strategies: broadcast messaging, reward systems, and network reconfiguration.
Results:
Survey findings indicated that higher AI trust is associated with increased risk of delays (OR = 1.09, P = 0.043), with usage frequency partially mediating this relationship (indirect OR = 1.24, P < 0.001). Chronic disease status further amplified these effects (OR = 1.42, P = 0.010). Simulation results demonstrated a bidirectional trust erosion phenomenon: delay rates decreased from 10.6% to 9.5%, while mean trust levels declined from 1.91 to 1.52 over 14 days, particularly among high-risk populations. Among the interventions tested, broadcast messaging was found to be the most effective in reducing delays (OR = 0.94, P < 0.001), whereas network reconfiguration led to increased risk due to "trust polarization" (OR = 1.04, P < 0.001).
Conclusions:
Trust in AI significantly contributes to healthcare delays through behavioral feedback loops, especially in the context of chronic diseases. System-wide risk-alert interventions are more effective than network-based strategies, highlighting the need for carefully designed trust-preserving mechanisms in healthcare AI to mitigate delays and improve patient outcomes.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.