Medical Tourism in the Context of Regional and Sectoral Economies: Evolution of Publications and Prediction of their Citation Rates Using Machine Learning Methods

Abstract

The relevance of the study is determined by the need to understand the dynamics of scientific publications and predict their citability in the rapidly developing field of medical tourism, which represents an important sector of regional economy. This research investigates the scientific evolution of publications in the dynamically changing area of health and wellness tourism. Special attention is paid to changes influenced by technological innovations, emergence of new directions and destinations for medical tourism, as well as transformation of behavior among international medical tourists. Despite significant growth of revenue from foreign medical tourism exceeding $700 million in 2022, actual medical tourism accounts for as little as about 5 % in Russia. Based on the official ranking of the International Medical Tourism Association (MTI), top destinations for medical tourism in 2020–2021 included Canada, Japan, Spain, Israel, etc. Reviewing present- day tendencies in global medical tourism in 2024, a clear new development vector emerges related to geopolitical centers of medical tourism forming within BRICS countries (Brazil, Russia, India, China, South Africa) and Global South region. Key factors driving medical tourism are accessibility of healthcare services, availability of digital technologies, and high-quality service provision for foreign patients. The aim of this work is to investigate the evolution of scientific publications on medical tourism and explore the possibility of creating a predictive model for citation using machine learning methods. The information base of the study consists of data from a large dataset including 1,535 publications from the Scopus database covering the period from 1952 to 2020. Statistical analysis methods and machine learning technologies implemented through the Python programming language in the cloud environment of Google Colab were used. The study made it possible to reveal the key trends in scientific publications on medical tourism, highlight the most important factors determining citations, and develop a version of publication citation prediction model. These results can be utilized to optimize strategies for the development of medical tourism in Russia, enhance its position as a center for medical tourism, strengthen the role of domestic science in international rankings, develop recommendations for improving quality and visibility of Russian studies, and form criteria for evaluating effectiveness of scientific works in the sphere of medical tourism. The study will be useful for researchers, teachers, practitioners, and healthcare organizers involved in interdisciplinary issues in medicine and tourism, particularly those focused on inbound medical and health-and-wellness tourism development in the Russian Federation.