Algorithmic decision-making and cultural nursing in digital health: Time, space, and the body
DOI:
https://doi.org/10.14687/jhs.v23i1.6621Keywords:
Teknoloji, dijital sağlık, kültürel hemşirelik, digital health, algorithmic decision-making, cultural nursing, artificial intelligence, care ethics, data governance, clinical judgment, care relationship, patient experience, patient autonomy, data-driven governance, zaman–mekan–beden, dijital toplum, algoritmik toplumAbstract
This study examines how algorithmic decision-making processes increasingly embedded in digital health systems transform nursing care and the care relationship from a cultural nursing perspective. Rather than conducting a systematic literature review, the article is designed as a theoretical narrative review that selectively and critically synthesizes interdisciplinary literature. Existing research on digital health transformations has largely focused on technological performance, managerial efficiency, and clinical outcomes. In contrast, the temporal, spatial, and embodied implications of these transformations for care remain relatively underexplored. The article discusses three fields of tension that become particularly salient from a cultural nursing perspective: the tension between clinical judgment and algorithmic decision-making, the tension between care ethics and data-driven governance, and the tension between time pressure and the quality of the care relationship. It argues that although algorithmic systems may offer important opportunities for healthcare delivery, they also risk rendering patients’ lived experiences, cultural contexts, and relational dimensions less visible. Cultural nursing is therefore presented as a critical care perspective that makes visible the distance between algorithmically produced data representations and patients' social and cultural realities.
Özet
Bu çalışma, dijital sağlık sistemlerine giderek daha fazla entegre olan algoritmik karar alma süreçlerinin, kültürel hemşirelik perspektifinden bakıldığında hemşirelik bakımını ve bakım ilişkisini nasıl dönüştürdüğünü incelemektedir. Makale, sistematik bir literatür taraması yapmak yerine, disiplinlerarası literatürü seçici ve eleştirel bir şekilde sentezleyen teorik bir anlatı incelemesi olarak tasarlanmıştır. Dijital sağlık dönüşümlerine ilişkin mevcut araştırmalar, büyük ölçüde teknolojik performans, yönetimsel verimlilik ve klinik sonuçlara odaklanmıştır. Buna karşın, bu dönüşümlerin bakım üzerindeki zamansal, mekansal ve somut etkileri nispeten az araştırılmıştır. Makale, kültürel hemşirelik perspektifinden özellikle öne çıkan üç gerilim alanını ele almaktadır: klinik yargı ile algoritmik karar verme arasındaki gerilim, bakım etiği ile veriye dayalı yönetişim arasındaki gerilim ve zaman baskısı ile bakım ilişkisinin kalitesi arasındaki gerilim. Makalede, algoritmik sistemlerin sağlık hizmetlerinin sunumu için önemli fırsatlar sunsa da, hastaların yaşam deneyimlerini, kültürel bağlamlarını ve ilişkisel boyutlarını daha az görünür hale getirme riski taşıdığı savunulmaktadır. Bu nedenle kültürel hemşirelik, algoritmik olarak üretilen veri temsilleri ile hastaların sosyal ve kültürel gerçeklikleri arasındaki mesafeyi görünür kılan kritik bir bakım perspektifi olarak sunulmaktadır.
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References
Abràmoff, M. D., Tobey, D., & Char, D. S. (2020). Lessons learned about autonomous ai: Finding a safe, efficacious, and ethical path through the development process. American Journal of Ophthalmology, 214, 134–142. https://doi.org/10.1016/j.ajo.2020.02.022
Balkin, J. M. (2017). 2016 Sidley Austin Distinguished Lecture on Big Data Law and Policy: The three laws of robotics in the age of big data. Ohio State Law Journal, 78(5), 1217–1241.
Benjamin, R. (2019). Race after technology: abolitionist tools for the new Jim Code. Polity Press.
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229
Couldry, N., & Mejias, U. A. (2019) . Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), 336–349. https://doi.org/10.1177/1527476418796632
Leininger, M. M., & McFarland, M. R. (Eds.). (2006). Culture care diversity and universality: A worldwide nursing theory (2nd ed.). Jones and Bartlett.
Lupton, D. (2018). Digital Health: Critical and Cross-Disciplinary Perspectives. Routledge. https://doi.org/10.4324/9781315648835
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Ronquillo, C., Peltonen, L.-M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Hardiker, N., Junger, A., Michalowski, M., Nyrönen, P., Ramis, M.-A., Reed, D., Salakoski, T., Suhonen, R., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Nursing Scholarship, 53(1), 86–94. https://doi.org/10.1111/jnu.12606
Schuilenburg, M., & Peeters, R. (2021). The algorithmic society: Technology, power, and knowledge. Routledge.
Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020). Hidden in plain sight—Reconsidering the use of race correction in clinical algorithms. The New England Journal of Medicine, 383(9), 874–882. https://doi.org/10.1056/NEJMms2004740
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