Ranking of AI-Driven Strategies for Optimizing the Health Tourism Supply Chain Using Stratified BWM

Abstract

Health tourism, as an emerging and strategic sector of the tourism industry, plays a significant role in economic growth, the development of international cooperation, and the enhancement of healthcare service quality. By integrating medical needs with recreational services, this sector holds great potential for attracting international patients. In this context, artificial intelligence technologies—including machine learning, data mining, natural language processing, and recommender systems—offer innovative opportunities to improve the efficiency of the health tourism supply chain. From enhancing patient data management to facilitating travel planning and personalizing services, AI can play a pivotal role in optimizing industry processes. This study aims to identify and rank the most effective AI-driven strategies for optimizing the health tourism supply chain. First, a comprehensive literature review and analysis of domain-specific sources were conducted to extract a set of key strategies. Subsequently, these strategies were evaluated and prioritized using the multi-criteria decision-making method known as the Stratified Best-Worst Method (Stratified BWM). The results revealed that the top three strategies were: “Patient Data Management and Analysis” (A1), “Healthcare supply chain optimization” (A2), and “Customer Experience and Digital Services” (A3). These strategies—by leveraging intelligent data usage, optimal resource allocation, and improved digital engagement with patients—significantly contribute to higher efficiency, customer satisfaction, and service quality. The findings of this research provide valuable insights for policymakers, healthcare managers, and tourism stakeholders in designing targeted programs and promoting the sustainable development of the health tourism supply chain.