SavedAdd your journal

Journals / Eurasian Journal of Health Technology Assessment / 2026 / Cilt: 9 Sayı: 1

The Impact of Artificial Intelligence on the Patient Journey in Medical Tourism: A Management Framework

Yapay Zekânın Sağlık Turizminde Hasta Yolculuğuna Etkisi: Yönetsel Bir Çerçeve Önerisi

Ufuk Burak Karcıoğlu(ISTINYE UNIVERSITY)0009-0006-9131-6407
Pages
58–67
Publication Date
2025-06-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This framework offers practical insights for stakeholders and lays the groundwork for future empirical research on AI integration in medical tourism management.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: Medical tourism presents unique opportunities for delivering patient-centered healthcare, yet the role of artificial intelligence (AI) in enhancing management processes within the patient journey remains underexplored.
📊
Key Findings & Data:Methods: A structured literature review was conducted, analyzing a total of 100 publications (95 peer-reviewed articles and 5 industry reports) published between 2007 and 2023.
💡
Conclusion & Impact:This framework offers practical insights for stakeholders and lays the groundwork for future empirical research on AI integration in medical tourism management.
🔬Design:Review & Systematic Survey
📍Scope:2007 Verileri
⚙️Variables:yapay zeka · sağlık turizm · medikal turizm

Abstract

Objective: Medical tourism presents unique opportunities for delivering patient-centered healthcare, yet the role of artificial intelligence (AI) in enhancing management processes within the patient journey remains underexplored. This study aims to investigate AI’s potential to optimize patient experience and operational efficiency in medical tourism by proposing a comprehensive conceptual framework. Methods: A structured literature review was conducted, analyzing a total of 100 publications (95 peer-reviewed articles and 5 industry reports) published between 2007 and 2023. Thematic analysis was employed to synthesize findings and develop the AI-Enhanced Medical Tourism Patient Journey Model (AI-MTPJM), integrating AI applications across key stages of the patient journey. Results: The AI-MTPJM framework outlines five main stages of the medical tourism patient journey: (1) Information Search, (2) Planning and Reservation, (3) Travel and Treatment, (4) Post-treatment Follow-up, and (5) Feedback and Loyalty. Various AI technologies such as virtual assistants, predictive analytics, real-time monitoring, telemedicine, and sentiment analysis are mapped to these stages. These tools contribute to personalized services, improved operational workflows, and enhanced patient satisfaction in international medical tourism destinations. Conclusion: The AI-MTPJM serves as a strategic bridge between patient-centered service design and data-driven healthcare management. While it holds significant potential for strengthening the competitiveness of medical tourism providers, challenges such as data privacy, cost, and infrastructure requirements must be carefully managed. This framework offers practical insights for stakeholders and lays the groundwork for future empirical research on AI integration in medical tourism management.

Özet

Özet Amaç: Sağlık turizmi, hasta odaklı sağlık hizmeti sunumu açısından benzersiz fırsatlar sunmaktadır. Ancak, yapay zekânın (YZ) hasta yolculuğu sürecindeki yönetim uygulamalarını iyileştirme potansiyeli yeterince araştırılmamıştır. Bu çalışma, sağlık turizminde hasta deneyimini ve operasyonel verimliliği artırmak amacıyla yapay zekânın potansiyelini incelemeyi ve kapsamlı bir kavramsal çerçeve önermeyi amaçlamaktadır. Yöntem: 2007 ile 2023 yılları arasında yayımlanmış toplam 100 yayının (95 hakemli makale ve 5 sektör raporu) analiz edildiği yapılandırılmış bir literatür taraması gerçekleştirilmiştir. Bulguların sentezlenmesi ve Yapay Zekâ Destekli Sağlık Turizmi Hasta Yolculuğu Modeli (YZ-STHYM) geliştirilmesi için tematik analiz yöntemi kullanılmıştır. Bu model, hasta yolculuğunun temel aşamalarında YZ uygulamalarının entegrasyonunu içermektedir. Bulgular: YZ-STHYM çerçevesi, sağlık turizmi hasta yolculuğunun beş ana aşamasını tanımlamaktadır: (1) Bilgi Arayışı, (2) Planlama ve Rezervasyon, (3) Seyahat ve Tedavi, (4) Tedavi Sonrası Takip ve (5) Geri Bildirim ve Sadakat. Sanal asistanlar, kestirimsel analizler, gerçek zamanlı izleme, uzaktan sağlık hizmetleri (tele-tıp) ve duygu analizi gibi çeşitli YZ teknolojileri bu aşamalara eşleştirilmiştir. Bu araçlar, kişiselleştirilmiş hizmetler, geliştirilmiş operasyonel iş akışları ve uluslararası sağlık turizmi destinasyonlarında hasta memnuniyetinin artırılmasına katkı sağlamaktadır. Sonuç: YZ-STHYM çerçevesi, hasta odaklı hizmet tasarımı ile veri odaklı sağlık yönetimi arasında stratejik bir köprü işlevi görmektedir. Sağlık turizmi sağlayıcılarının rekabet gücünü artırma potansiyeli taşımasına rağmen, veri gizliliği, maliyet ve altyapı gereksinimleri gibi zorlukların dikkatle yönetilmesi gerekmektedir. Bu çerçeve, paydaşlar için pratik içgörüler sunmakta ve sağlık turizmi yönetiminde yapay zekâ entegrasyonuna yönelik gelecekteki ampirik araştırmalar için zemin hazırlamaktadır.

🔖

Cite This Article

Karcıoğlu, U. B. (2025). The Impact of Artificial Intelligence on the Patient Journey in Medical Tourism: A Management Framework. Eurasian Journal of Health Technology Assessment, Cilt: 9 Sayı: 1, 58–67. https://doi.org/10.52148/ehta.1701664

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (19)

  1. Bathla, G., Raina, A., & Rana, V. S. (2024). Artificialintelligence-drivenenhancements in medicaltourism: Opportunities, challenges, andfutureprospects. In V. Hassan, A. Albattat, & S. Basheer (Eds.), Impact of AI andRobotics on theMedicalTourismIndustry (pp. 139–162). IGI Global. https://doi.org/10.4018/978-1-6684-8653-0.ch007
  2. Berry, L. L., &Bendapudi, N. (2007). Healthcare: A fertilefieldfor service research. Journal of Service Research, 10(2), 111–122. https://doi.org/10.1177/1094670507306682
  3. Buhalis, D., &Law, R. (2008). Progress in informationtechnologyandtourismmanagement: 20 years on and 10 yearsafterthe Internet—Thestate of eTourismresearch. Tourism Management, 29(4), 609–623. https://doi.org/10.1016/j.tourman.2008.01.005
  4. Connell, J. (2013). Contemporarymedicaltourism: Conceptualisation, cultureandcommodification. Tourismmanagement, 34, 1-13.https://doi.org/10.1016/j.tourman.2012.05.009
  5. Davenport, T. H., &Ronanki, R. (2018). Artificialintelligencefortherealworld. Harvard Business Review, 96(1), 108–116.
  6. Fottler, M. D., Ford, C. R., & Ford, S. (2011). Patient-centeredcareandhospitalperformance: A frameworkforevaluatingtheeffects of patient-centeredcare in hospitals. Journal of Healthcare Management, 56(1), 55–70.
  7. GlobalData. (2023). MedicalTourism Market Analysis andForecast, 2023–2028. GlobalDataPlc.
  8. Hanefeld, J., Lunt, N., Smith, R., &Horsfall, D. (2015). Why do medicaltouriststraveltowherethey do? The role of networks in determiningmedicaltravel. SocialScience&Medicine, 124, 356–363. https://doi.org/10.1016/j.socscimed.2014.05.016
  9. Huang, M.-H., &Rust, R. T. (2021). Engagedto a robot? The role of AI in service. Journal of Service Research, 24(1), 30–48. https://doi.org/10.1177/1094670520960065
  10. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S.,. &Wang, Y. (2017). Artificialintelligence in healthcare: Past, presentandfuture. StrokeandVascularNeurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
  11. Keskin, E., & Atik, H. (2023). Artificialintelligence in healthcaremanagement: Applications in Turkishmedicaltourism. Journal of HealthSystemsandPolicies, 5(1), 23–35.
  12. Lunt, N., Horsfall, D., Hanefeld, J., & Smith, R. (2011). Medicaltourism: Treatments, marketsandhealthsystemimplications: A scopingreview. OECD HealthWorkingPapers, No. 44. OECD Publishing. https://doi.org/10.1787/5k41z7g081r8-en
  13. OECD. (2023). Digitalhealthdisparities in developingcountries. OECD Publishing.
  14. Parasuraman, A., Zeithaml, V. A., &Berry, L. L. (1988). SERVQUAL: A multiple-itemscaleformeasuringconsumerperceptions of service quality. Journal of Retailing, 64(1), 12–40.
  15. Shafik, W. (2024). Artificialintelligenceandthemedicaltourism. InExaminingTouristBehaviorsandCommunityInvolvement in DestinationRejuvenation (pp. 207–233). IGI Global. https://doi.org/10.4018/978-1-6684-8541-0.ch011
  16. Tan, C. H., & Lee, S. Y. (2024). AI-drivenlogistics in medicaltourism: TheSingaporeexperience. International Journal of TourismResearch, 26(2), 112–125. https://doi.org/10.1002/jtr.2550
  17. Tripathi, R. P. (2023, November). Harnessingthepotential of AI and ML in medicaltourism: Challenges, opportunities, andethicalimplications. In 2023 3rd International Conference on TechnologicalAdvancements in ComputationalSciences (ICTACS) (pp. 1068–1072). IEEE. https://doi.org/10.1109/ICTACS57802.2023.10391284
  18. Topol, E. J. (2019). High-performancemedicine: Theconvergence of humanandartificialintelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
  19. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S.,. &Wang, Y. (2017). Artificialintelligence in healthcare: Past, presentandfuture. StrokeandVascularNeurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101