Research on AI-Driven Whole-Process Service Mode in Cross-Border Medical Tourism
Zhuorong Li
Pinnacle Academic Press Proceedings Series, 2026, vol. 11, 210-216
Abstract:
With rapid advances in global medical technology and significant improvements in people's health awareness, international medical tourism has gradually emerged as an increasingly important mode of healthcare service delivery worldwide. The primary purpose of this paper is to comprehensively discuss the application of artificial intelligence (AI) throughout the entire service process of international medical tourism. Furthermore, it aims to construct an innovative, AI-based operating model by systematically analyzing patients' diverse needs, the optimal allocation of global medical resources, and continuous service quality improvement. Studying the integration of AI technology helps to effectively consolidate and optimize healthcare resources, significantly improve patients' personalized service experiences, and substantially reduce the inherent risks caused by information asymmetry between providers and patients. To achieve this overarching goal, this paper proposes a robust, data-driven decision-support system tailored for the healthcare sector. The proposed system can analyze complex market dynamics in real time and dynamically optimize international medical services to meet evolving demands. Comprehensive data analysis based on rigorous empirical research verifies the remarkable effectiveness of the AI-driven service model in improving both the operational efficiency of international medical tourism and overall patient satisfaction. Ultimately, this study provides solid theoretical support and valuable practical guidance for the sustainable development and digital transformation of related fields in the global healthcare industry.
Keywords: artificial intelligence; medical tourism; international healthcare; operating model; data-driven (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:dba:pappsa:v:11:y:2026:i::p:210-216
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