The Strategic Efficacy of Artificial Intelligence (AI) in Medical Tourism

The Strategic Efficacy of Artificial Intelligence (AI) in Medical Tourism

Olabode Gbobaniyi, Daniela Tincani, Peter Emelone
Copyright: © 2024 |Pages: 40
DOI: 10.4018/979-8-3693-2248-2.ch005
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Abstract

AI's integration and adoption in the sector have evolved to be a game-changer through operational revolutionisation regarding accessibility to advanced diagnosis and treatments, reduced waiting times, and cost savings. This chapter explores the strategic efficacy of AI in the context of medical tourism. Using the term “strategic efficacy,” the authors encompass the concept of efficiency and effectiveness of AI in achieving a strategic outcome in medical tourism. The authors' purviews are that is important to ensure that an AI strategy in medical tourism not only looks good on paper but also continues to produce high success for the global practice. In this chapter, the authors discuss AI's emergence in the medical tourism industry, the strategic efficacy of AI in medical tourism, the categories of AI-system devices used in medical tourism, and the AI-system devices. Also discussed are AI systems applications to some major diseases in medical tourism.
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1. Introduction

Medical tourism is a global phenomenon highly dependent on innovation and technology that has continued to develop (Subasinghe et al., 2020). Brazil, China, Costa Rica, India, Iran, Mexico, Panama, Singapore, South Africa, Taiwan, and Thailand are among the popular travel destinations for medical tourism (Lo, 2020). Travel firms and websites specialising in medical tourism sometimes promote package arrangements that portray recovery following surgery as a holiday. Offerings include psychiatric therapies and procedures like gender-reassignment surgeries that could be socially or culturally inappropriate and consequently inaccessible in other nations, as well as cosmetic, cardiac, eye, dental, and orthopaedic surgery (Rogers, 2023). Patients Beyond Borders indicated that the global medical tourism market was worth between $65 and $87.5bn, but by 2027, it is expected to grow to about $207bn (Woodman, 2019). A wide range of medical procedures from cosmetic surgery and dentistry to cardiovascular procedures and experimental cancer treatments, have been identified to inspire medical tourism (Woodman, 2015). However, following the impact of COVID-19 in 2020, the United Nations World Tourism Organisation (UNWTO) estimated that the medical tourism sector had a 60 to 80 per cent decline, thus naming COVID-19 as the worst crisis that had ever confronted the industry (UNWTO, 2020a, 2020b). With the global economies crawling out from the devastating impact of COVID-19, the health and tourism industries in many affected countries have also increasingly continued to promote investments in technological advancements to the medical tourism sector in their attempt to increase patients' patronage of their country (Tileva, 2021). Simply as it is named, Ghassemi et al. (2020) described medical tourism as a process whereby patients travel outside of their country of residence to another country for medical care.

It is also important to emphasise that medical tourism is related to the broader notion of health tourism which, in some countries, has longstanding historical antecedents of spa towns, coastal localities, and other therapeutic landscapes (e.g., Greece, Turkey, Croatia) (Meghani, 2011). Some literature has considered both health and medical tourism as a combined phenomenon but with different emphases. For instance, Carrera and Bridges (2006) define health tourism as the organised travel outside one’s local environment for the maintenance, enhancement, or restoration of an individual’s well-being in mind and body. However, it can be said that Carrera and Bridges’ definition encompasses medical tourism which is delimited to organised travel outside one’s natural healthcare jurisdiction for the enhancement or restoration of the individual’s health through medical intervention (Connel & Page, 2019; Lunt, et al., 2011).

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