Document Type : Research Article
Authors
Department of Geography and Urban Planning, Faculty of Letters and Humanities, Shahid Chamran University of Ahvaz, Ahvaz, Iran
10.22059/jut.2024.377844.1216
Abstract
A B S T R A C T
As a transformative force in medical tourism, Artificial intelligence (AI) has driven this sector to new developments. AI in medical tourism has not only facilitated the delivery of quality treatments but also simplified the overall patient experience and brought about significant changes in the dynamics of this industry. Integrating intelligent healthcare systems that utilize AI technologies is essential in the healthcare ecosystem, especially in medical tourism monitoring. In addition, using AI helps reduce treatment costs and simplifies the information workflow. Therefore, applying advanced technologies and AI has increased healthcare services’ efficiency, effectiveness, and cost-effectiveness, which benefits providers and medical tourists. The study mainly aimed to present a model to identify and rank factors affecting AI use in medical tourism development in Ahvaz. The applied study employed a descriptive-analytical research method. The statistical population consisted of 50 experts from the tourism sector and the medical sector, and due to the limited population, all people were selected as samples. The research results showed that the technological dimension had the highest weight, with a score of 0.2021. After that, the economic dimension, with a score of 0.2043; communication and information, with a score of 0.2021; infrastructure, with a score of 0.1971; and social, with a score of 0.1864, took the second to fifth positions.
Extended Abstract
Introduction
As a transformative force in medical tourism, Artificial intelligence (AI) has driven this sector to new developments. AI in medical tourism has not only facilitated the delivery of quality treatments but also simplified the overall patient experience and brought about significant changes in the dynamics of this industry. Integrating intelligent healthcare systems that utilize AI technologies is essential in the healthcare ecosystem, especially in medical tourism monitoring. In addition, using AI helps reduce treatment costs and simplifies the information workflow. Therefore, applying advanced technologies and AI has increased healthcare services’ efficiency, effectiveness, and cost-effectiveness, which benefits providers and medical tourists. The study mainly aimed to present a model to identify and rank factors affecting AI use in medical tourism development in Ahvaz. The applied study employed a descriptive-analytical research method.
Methodology
The primary research goal is to analyze artificial intelligence (AI) use in medical tourism development with the DEMATEL-Based Analytic Network Process (DANP) Model. Thus, an exploratory mixed methods sequential design method was employed; qualitative and quantitative research methods were sequenced. Based on this, in the first part of the research, which is qualitative, an in-depth review of the literature was used to determine AI use analysis in medical tourism development. In the second part, which is quantitative, the DANP was used to prioritize the variables. As mentioned, the first part of this research is dedicated to the qualitative part. A deep literature review method is used in this part of the research. The second part of this research is dedicated to the quantitative part. In this research, the findings of the qualitative phase were used, and the DANP model was used to investigate the relationship between the studied areas and their weighting. To collect information, using the dimensions and criteria of the qualitative part, the DEMATEL-based structured questionnaire based on the network analysis approach was employed, which was completed in person or via virtual submission. The nature of the questionnaires was such that they included a table of components, and the relationship and the effect of these components were obtained from experts regarding the appropriateness of the questionnaires and applied. In addition, the questionnaires were standard and used in similar studies.
Post-test reliability was used to measure reliability. After some time, after sending the questionnaires, they were resent to five experts participating in the research to ensure this. The answers’ correlation is at least 70%, and the probability value is less than 0.5, indicating the questionnaires’ acceptable reliability. The reliability of the questionnaire pre-test/post-test was determined using the software. Cronbach’s alpha of this research is equal to 0.89, which shows a good value. The statistical population consisted of experts in tourism and the medical sector. The inclusion criteria of 50 experts selected via a non-random and purposive sampling technique were as follows: holder of a master’s degree and above, availability of tourists, willingness to participate in research, enough time to understand the questionnaire correctly, and complete mastery of the field of AI in medical tourism development.
Results and discussion
The prioritization of the research dimensions based on the obtained weights demonstrated that the technological dimension had the highest weight, with a score of 0.2021. After that, the economic dimension, with a score of 0.2043; the communication and information dimension, with a score of 0.2021; the infrastructure, with a score of 0.1971; and the social dimension, with a score of 0.1864, took the second to fifth ranks. According to these dimensions, among the indicators affecting AI use on medical tourism development in Ahvaz, the technological and economic dimensions are significant and consistent with the research literature.
According to the research criteria prioritization and the obtained weights, the most weight is given to the indicators “improving patients’ experience by providing round-the-clock support to patients by answering their questions and providing useful information through artificial intelligence-based chatbots” (C3) with a score of 0.0733, “helping patients plan their trip including booking flights, hotels, and transportation” (F3) with a score of 0.0716, acceleration of remote medicine and diagnosis and treatment services (C1) with a score of 0.0708, ranked the first to third place among the indicators affecting AI use on medical tourism development in Ahvaz city. According to the survey of experts, these indicators have the highest scores. Also, the indicators “providing translation services for different languages and dialects and helping patients to understand medical terms and methods” (B1) with a score of 0.0252, “helping patients to contact family and friends at home and sharing their opinions with other potential medical tourists” (B2) with a score of 0.0345, and “medical tourism as an instrument for developing international relations with other countries” (D1) with a score of 0.0380 have the lowest scores. Thus, authorities need to pay much attention to them.
Conclusion
Employing the DNAP model distinguished the present study from other studies. The research results from the DANP Model showed that the technological dimension had the most weight, with a score of 0.2021. After that, the economic dimension with a score of 0.2043, the communication and information dimension with a score of 0.2021, the infrastructural dimension with a score of 0.1971, and the social dimension with a score of 0.1864 took the second to fifth ranks. The degree of influence and dependency, dimensions and criteria that interact more with other dimensions and criteria, and the weight of each dimension and component in the intelligent organization are determined. In total, 5 dimensions and 20 components apply artificial intelligence to medical tourism, and the weight of each of the dimensions and components was identified as described above to evaluate AI use and medical tourism in Ahvaz city.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.
Keywords
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