EFFECTIVE USE OF BIG DATA IN HEALTH SERVICES: THE EXAMPLE OF HES AND WECHAT

Yıl 2025, Cilt: 11 Sayı: 2, 139 - 166

Rumeysa Nur Tamyiğit , Furkan Ahmet Tamyiğit

Öz

With the advancement of computer technologies, data production has significantly increased, and this data is utilized in areas such as forecasting, disaster and epidemic management, and decision-making processes. The interpretation of data collected and stored through smart systems, sensors, and cloud computing infrastructures has become increasingly critical. The integration of artificial intelligence and cloud technologies enhances the efficiency of data analysis, offering substantial benefits for public safety and healthcare systems. However, insufficient or inaccurate data analysis can lead to serious consequences, including economic losses, flawed administrative decisions, and disruptions in healthcare services.

Anahtar Kelimeler

: Big Data , Data Analytics , Epidemic , COVID-19 , Healthcare Sector

SAĞLIK HİZMETLERİNDE BÜYÜK VERİNİN ETKİN KULLANIMI: HES VE WECHAT ÖRNEĞİ

Öz

Gelişen bilgisayar teknolojileriyle birlikte veri üretiminde büyük bir artış yaşanmakta; bu veriler, öngörüleme, afet ve salgın yönetimi ile karar alma süreçlerinde kullanılmaktadır. Akıllı sistemler, sensörler ve bulut bilişim altyapıları aracılığıyla elde edilen verilerin anlamlandırılması gün geçtikçe daha kritik hâle gelmiştir. Yapay zekâ ve bulut teknolojilerinin entegrasyonu, veri analizinde etkinliği artırmakta, böylece kamu güvenliği ve sağlık sistemleri açısından önemli avantajlar sağlamaktadır. Ancak veri analizinin yetersiz veya hatalı yapılması durumunda ekonomik kayıplar, hatalı yönetim kararları ve sağlık hizmetlerinde aksaklıklar gibi ciddi sorunlar ortaya çıkabilmektedir. Dolayısıyla, özellikle pandemi benzeri kriz dönemlerinde, büyük verinin doğru, güvenilir ve etkin bir şekilde işlenmesi, stratejik bir gereklilik olarak öne çıkmaktadır. Büyük veri, Covid-19 pandemisi sırasında sıklıkla kullanılmıştır. Virüsün tüm ülkelere yayılmasının ardından pandemi ilan edilmiş ve ülkeler, bulaşıcı hastalıkla mücadele için büyük veri teknolojisinden yararlanmıştır. Araştırmada, Covid-19 pandemisi sırasında büyük verinin ne kadar kullanıldığını ve bunun ne gibi faydalar sağladığını incelenmektedir.

Anahtar Kelimeler

Büyük Veri , Veri Analitiği , Salgın , Covid-19 , Sağlık Sektörü

Kaynakça

• Allen Institute for AI. (2020). CORD-19: COVID-19 Open Research Dataset. https://www.semanticscholar.org/cord19
• Alsunaidi, S. J., Almuhaideb, A. M., Ibrahim, N. M., Shaikh, F. A., Alqudaihi, K. S., Alhaidari, F. A., Khan, I. U., Aslam, N., & Alshahrani, M. S. (2021). Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors, 21(7):2282, 1-24.
• Benedictow, O. J. (2004). The Black Death, 1346-1353: the complete history. Boydell & Brewer.
• Briggs, C. L., & Mantini-Briggs, C. (2009). Confronting health disparities: Latin American social medicine in Venezuela. American journal of public health, 99(3), 549-555.
• Castells, M. (2009). Communication power. Oxford University Press.
• Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. https://doi.org/10.1007/s11036-013-0489-0
• Cheng, Y., Luo, H., Hu, H., & Shi, Y. (2022). Early warning of COVID-19 from social media in China: WeChat keyword analysis. Journal of Risk Research, 25(1), 93–109. https://doi.org/10.1080/13669877.2021.2021257
• Citizen Lab. (2020). Censored contagion: How information on the coronavirus is managed on Chinese social media. University of Toronto. https://citizenlab.ca/2020/03/censored-contagion-how-information-on-the-coronavirus-is-managed-on-chinese-social-media/
• Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., ... & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 16598. https://doi.org/10.1038/s41598-020-73510-5
• Crosby, F. J., Pufall, A., Snyder, R. C., O’Connell, M., & Whalen, P. (1989). The denial of personal disadvantage among you, me, and all the other ostriches. In Gender and thought: Psychological perspectives (pp. 79-99). New York, NY: Springer New York.
• Demir, F., Özdaş, D. F., & Çakmak, M. (2022). COVID-19 salgın sürecinin eğitime yansımaları. Eğitim ve İnsani Bilimler Dergisi: Teori ve Uygulama, 13(26), 275–300.
• DeepMind. (2020). AlphaFold: Using AI for scientific discovery. https://deepmind.com/research/highlighted-research/alphafold
• Diamond, D. W. (1997). Liquidity, banks, and markets. Journal of Political Economy, 105(5), 928-956.
• FourWeekMBA. (2025). Key characteristics of new media. https://fourweekmba.com/new-media
• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
• Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.
• Kumar, S., Morstatter, F., & Liu, H. (2014). Twitter data analytics. In Proceedings of the 23rd International Conference on World Wide Web, 247–250.
• Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. META Group Research Note, 6(70), 1.
• Manovich, L. (2001). The language of new media. MIT Press.
• McLuhan, M. (1964). Understanding media: The extensions of man. McGraw-Hill.
• Murray, J. H. (2011). Inventing the medium: Principles of interaction design as a cultural practice. MIT Press.
• Nguyen, N. T., Nguyen, T. P., Nguyen, T. T., & Van Nguyen, H. (2020). Big data analytics and COVID-19 pandemic: A comprehensive review. Journal of Big Data, 7(1), 81. https://doi.org/10.1186/s40537-020-00332-6
• PMC. (2020). A survey of Big Data dimensions vs Social Networks analysis. PubMed Central.
• Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 1–10. https://doi.org/10.1186/2047-2501-2-3
• Tong, H., Li, Y., & Chen, M. (2020). Artificial intelligence in COVID-19 drug repurposing. Frontiers in Pharmacology, 11, 818. https://doi.org/10.3389/fphar.2020.00818
• U.S. Department of Energy. (2020). COVID-19 High Performance Computing Consortium. https://COVID19-hpc-consortium.org
• Van Dijck, J., Poell, T., & De Waal, M. (2018). The platform society: Public values in a connective world. Oxford university press.
• Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing. JAMA, 323(14), 1341–1342. https://doi.org/10.1001/jama.2020.3151
• Yıldırım, T., & Kara, M. (2022). Yapay zekâ destekli tanı sistemleri: Türkiye'de kullanım alanları ve etkileri. Sağlık Bilimleri Dergisi, 10(2), 123–135.
• Zhang, L., Lin, D., & Sun, X. (2020). Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science, 368(6489), 409–413. https://doi.org/10.1126/science.abb3405
• Zhang, Y., Liu, Z., & Zhang, X. (2021). Application of big data and artificial intelligence in epidemic surveillance and containment. Intelligent Medicine, 1(1), 1–11. https://doi.org/10.1016/j.imed.2022.10.003
• Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., ... & Tan, W. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England journal of medicine, 382(8), 727-733.
• Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 1–6. https://doi.org/10.1177/2053951714559253
• Zwitter, A., & Gstrein, O. J. (2020). Big data, privacy and COVID-19 – Learning from humanitarian expertise in data protection. Journal of International Humanitarian Action, 5(4). https://doi.org/10.1186/s41018-020-00072-6