A Dual-Drive Recommendation Model for Smart Healthcare Platforms: Synergizing Proactive Search and AI-Driven Decision-Making

The emergence of smart healthcare platforms has significantly enhanced the accessibility of medical services, yet it has also introduced critical challenges such as information overload and patient decision-making dilemmas. This study investigates the interaction and synergistic optimization of a dual-drive mechanism—comprising ‘patient proactive search’ and ‘artificial intelligence (AI)-driven recommendations’—within healthcare platform recommendation systems. By developing a game-theoretic model that incorporates heterogeneous users (including random single-search users and rational multi-stage decision-makers) and competitive medical institutions, we systematically analyze how different recommendation strategies influence market equilibrium, patient utility, and platform profit. The findings reveal that in the absence of AI-driven recommendations, a higher proportion of random users intensifies price competition among providers. In contrast, the integration of AI-driven recommendations with proactive search behavior effectively mitigates price wars and enhances matching efficiency. Furthermore, our analysis identifies an optimal recommendation strategy weight that enables the platform to simultaneously improve both equilibrium price and user demand. This research offers a theoretical foundation for the design of efficient and sustainable recommendation systems in smart healthcare platforms and provides practical managerial insights for improving medical service efficiency and optimizing resource allocation.