AI-Based Precision Prediction of Healthcare Workers’ Antibiotic Use Intentions: Multi-theoretical Psychological and Behavioral Modeling

Introduction: Antimicrobial resistance (AMR) remains a global health priority, partly driven by irrational antibiotic prescribing among healthcare workers. Existing interventions often overlook the psychological and contextual factors that shape prescribing behaviour. This study aimed to integrate behavioural theory with explainable machine learning to identify psychological predictors of antibiotic use intention among clinicians. Methods: A cross-sectional survey was conducted among 1,135 healthcare workers from four public hospitals in China. Participants completed questionnaires based on constructs from the Theory of Planned Behavior, Health Belief Model, Rational Action Theory, Self-Efficacy Theory, Social Support Theory, and cognitive processing frameworks. LASSO regression and SHAP analysis were applied alongside machine learning classifiers (e.g. XGBoost, LightGBM, CatBoost) to identify key predictors and interactions influencing prescribing intention. Results: Social support, cognitive processing, knowledge and skills, and health beliefs were the most important predictors. SHAP analysis revealed nonlinear interactions, particularly between social support and cognitive engagement. Ensemble models achieved high predictive accuracy (F1-scores >0.94) for high and medium prescribing intention, but classification of low-intention individuals remained more challenging. Conclusion: Combining behavioural theory with explainable AI offers a scalable approach to identifying clinicians at risk of irrational prescribing. These findings support the development of psychologically tailored, real-time interventions that can improve antibiotic stewardship and address AMR in diverse health system settings.