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INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HEALTH SERVICES: EVALUATION OF SOCIO-TECHNICAL FACTORS USING SWARA AND AHP METHODS

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 94 - 108, 20.07.2025

Emre Yılmaz , Yeter Uslu

Abstract

This study aims to determine the socio-technical factors affecting the integration of artificial intelligence (AI) in healthcare services, prioritize them with SWARA and AHP methods, and provide solution proposals for the harmonizing artificial intelligence. Designed as a descriptive and cross-sectional study, a literature review identified ten main categories of socio-technical factors influencing AI integration in healthcare. According to the results of the SWARA and AHP analyses; the most important socio-technical factors affecting the integration of artificial intelligence in healthcare services were determined to be data quality and security, suitability of technological infrastructure, and skills and education, respectively. The results showed that both methods offered similar prioritization outcomes. Healthcare service providers should primarily develop strategies in line with these factors and allocate their resources in this direction. In order to increase data quality and security, integration of electronic health records and other data sources should be ensured, and data verification and cleaning mechanisms should be established to prevent incomplete or incorrect data. It is important to develop technological solutions such as cloud-based data storage and processing systems, high-performance computing infrastructures, and network systems that provide fast data flow. Moreover, professional development programs that encourage continuous learning should be organized to increase awareness of artificial intelligence technologies, improve artificial intelligence literacy, and enable them to use the systems effectively.

Keywords

Healthcare Artificial Intelligence Socio-Technical Factors SWARA AHP

References

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SAĞLIK HİZMETLERİNDE YAPAY ZEKA ENTEGRASYONU: SOSYO-TEKNİK FAKTÖRLERİN SWARA VE AHP YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 94 - 108, 20.07.2025

Emre Yılmaz , Yeter Uslu

Abstract

Bu çalışmada, sağlık hizmetlerinde yapay zekanın entegrasyonunu etkileyen sosyo-teknik faktörlerin belirlenerek SWARA ve AHP yöntemleriyle önceliklendirilmesi ve yapay zekanın uyumlaştırılmasına yönelik çözüm önerilerinin sunulması amaçlanmıştır. Betimsel ve kesitsel nitelikte olan çalışmada literatür taraması sonucunda sağlık hizmetlerinde yapay zekanın entegrasyonunu etkileyen sosyo-teknik faktörler 10 ana başlık altında toplanmıştır. SWARA ve AHP sonuçlarına göre; sağlık hizmetlerinde yapay zeka entegrasyonunu etkileyen sosyo-teknik faktörlerden en önemlileri sırasıyla veri kalitesi ve güvenliği, teknolojik altyapı uygunluğu ve beceri ve eğitim olarak tespit edilmiştir. Sonuçlar, her iki yöntemin de benzer önceliklendirme çıktıları sunduğunu göstermiştir. Sağlık hizmet sunucuları öncelikli olarak bu faktörler doğrultusunda strateji geliştirerek kaynaklarını bu yöne tahsis etmelidir. Veri kalitesi ve güvenliğinin artırılması için elektronik sağlık kayıtları ve diğer veri kaynaklarının entegrasyonu sağlanmalı, eksik ya da hatalı verilerin önüne geçmek için veri doğrulama ve temizleme mekanizmaları oluşturulmalıdır. Bulut tabanlı veri saklama ve işleme sistemleri, yüksek performanslı bilişim altyapıları ve hızlı veri akışını sağlayan ağ sistemleri gibi teknolojik çözümlerin geliştirilmesi önemlidir. Yapay zeka teknolojilerine yönelik farkındalığını artırmak, yapay zeka okuryazarlığını geliştirmek ve sistemleri etkin kullanmalarını sağlamak için sürekli gelişimi teşvik eden mesleki eğitim programları düzenlenmelidir.

Keywords

Sağlık Hizmeti Yapay Zeka Sosyo-teknik Faktörler SWARA AHP

References

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  • Aldwean, A. & Tenney, D. (2024). Artificial intelligence in healthcare sector: A literature review of the adoption challenges. Open Journal of Business and Management, 12(01), 129-147. https://doi.org/10.4236/ojbm.2024.121009
  • Alinezhad, A. & Khalili, J. (2019). SWARA method. New methods and applications in multiple attribute decision making (MADM). Springer. 99-102. https://doi.org/10.1007/978-3-030-15009-9_14
  • Amram, D., Cignoni, A., Banfi, T. & Ciuti, G. (2022). From P4 medicine to P5 medicine: Transitional times for a more human-centric approach to AI-based tools for hospitals of tomorrow. Open Research Europe, 2, 33. https://doi.org/10.12688/openreseurope.14524.1
  • Dai, T. & Tayur, S. (2022). Designing AI-augmented healthcare delivery systems for physician buy-in and patient acceptance. Production and Operations Management, 31(12), 4443-4451. https://doi.org/10.1111/poms.13850
  • Erdemir, N., Öztürk, F. & Kaya, GK. (2022). Kamu personeli performans değerlendirmesi için AHP ve bulanık TOPSIS ile bütünleşik karar destek modeli. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37(4), 1809-1822. https://doi.org/10.17341/gazimmfd.933793
  • Fazakarley, CA., Breen, M., Leeson, P., Thompson, B. & Williamson, V. (2023). Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives. BMJ Open, 13(12), e076950. https://doi.org/10.1136/bmjopen-2023-076950
  • Gedikli, E. & Kocaman, E. (2025). Priorities for effective management of health expenditures in OECD countries: fuzzy AHP application. Sosyoekonomi, 33(63), 11-29.https://doi.org/10.17233/sosyoekonomi.2025.01.01
  • Gerlich, M. (2024). Public anxieties about AI: Implications for corporate strategy and societal impact. Administrative Sciences, 14(11), 288. http://dx.doi.org/10.2139/ssrn.4972893
  • Habib, MM, Hoodbhoy, Z. & Siddiqui, MAR. (2024). Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLOS Digital Health, 3(5), e0000443. https://doi.org/10.1371/journal.pdig.0000443
  • Hashemkhani Zolfani, S. & Bahrami, M. (2014). Investment prioritizing in high-tech industries based on SWARA-COPRAS approach. Technological and Economic Development of Economy, 20(3), 534-553. https://doi.org/10.3846/20294913.2014.881435
  • Hashemkhani Zolfani, S., Yazdani, M. & Zavadskas, EK. (2018). An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing, 22, 7399-7405. https://doi.org/10.1007/s00500-018-3092-2
  • Hoseini, M. (2023). Patient experiences with AI in healthcare settings. AI and Tech in Behavioral and Social Sciences, 1(3), 12-18. https://doi.org/10.61838/kman.aitech.1.3.3
  • İnce, Ö., Bedir, N. ve Eren, T. (2016). Hastane kuruluş yeri seçimi probleminin AHP ile modellenmesi: Tuzla ilçesi uygulaması. Gazi Sağlık Bilimleri Dergisi, 1(3), 8-21.
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
  • Karalis, VD. (2024). The integration of artificial intelligence into clinical practice. Applied Biosciences, 3(1), 14-44. https://doi.org/10.3390/applbiosci3010002
  • Karami, S., Mousavi, SM & Antucheviciene, J. (2023). Enhancing contractor selection process by a new interval-valued fuzzy decision-making model based on SWARA and CoCoSo methods. Axioms, 12(8), 729. https://doi.org/10.3390/axioms12080729
  • Karamollaoğlu, H., Yücedağ, İ. & Doğru, İ. (2021). Risk assessment for electricity generation management process with SWARA-based Fuzzy TOPSIS method. Politeknik Dergisi, 27(1), 69-79. https://doi.org/10.2339/politeknik.917535
  • Kaye, J., Shah, N., Kogetsu, A., Coy, S., Katirai, A., Kuroda, M., ... ve Yamamoto, BA. (2024). Moving beyond technical issues to stakeholder involvement: Key areas for consideration in the development of human-centred and trusted AI in healthcare. Asian Bioethics Review, 1-11. https://doi.org/10.1007/s41649-024-00300-w
  • Keršuliene, V., Zavadskas, EK. & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258 https://doi.org/10.3846/jbem. 2010.12
  • Khalf, AG, Abdelhafez, K. & Khalab, S. (2022). Health care providers’ perception about artificial intelligence applications. Assiut Scientific Nursing Journal, 0(0), 0-0. https://doi.org/10.21608/asnj.2022.144712.1397
  • Koo, TH, Zakaria, AD, Ng, JSW & Leong, XB. (2024). Systematic review of the application of artificial intelligence in healthcare and nursing care. Malaysian Journal of Medical Sciences, 31(5), 135-142. https://doi.org/10.21315/mjms2024.31.5.9
  • Kumar, Y., Koul, A., Singla, R. & Ijaz, MF. (2023). Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459-8486. https://doi.org/10.1007/s12652-021-03612-z
  • Lacson, R., Cochón, L., Ip, IK, Desai, S., Kachalia, A., Dennerlein, JT … & Khorasani, R. (2019). Classifying safety events related to diagnostic imaging from a safety reporting system using a human factors framework. Journal of the American College of Radiology, 16(3), 282-288. https://doi.org/10.1016/j.jacr.2018.10.015
  • Long, P., Lu, L., Chen, Q., Chen, Y., Li, C. & Luo, X. (2023). Intelligent selection of healthcare supply chain mode – An applied research based on artificial intelligence. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1310016
  • Mamat, NJZ. & Daniel, JK. (2007). Statistical analyses on time complexity and rank consistency between singular value decomposition and the duality approach in AHP: A case study of faculty member selection. Mathematical and Computer Modelling, 46(7-8), 1099-1106. https://doi.org/10.1016/j.mcm.2007.03.025
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There are 55 citations in total.

Details

Primary Language Turkish
Subjects Operations Research
Journal Section Articles
Authors

Emre Yılmaz 0000-0003-4502-9846

Yeter Uslu 0000-0002-8529-6466

Publication Date July 20, 2025
Submission Date March 5, 2025
Acceptance Date June 29, 2025
Published in Issue Year 2025 Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ

Cite

APA Yılmaz, E., & Uslu, Y. (2025). SAĞLIK HİZMETLERİNDE YAPAY ZEKA ENTEGRASYONU: SOSYO-TEKNİK FAKTÖRLERİN SWARA VE AHP YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ), 94-108. https://doi.org/10.35379/cusosbil.1652007
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