Sağlık Hizmetlerinde Etik Veri Madenciliği: Mesleki Temellere Dayalı Bir Çerçeveye Doğru

Öz

Veri madenciliği teknolojileri, günümüz sağlık hizmetlerinde klinik karar verme, bireyselleştirilmiş tedavi ve sağlık sistemlerinin optimizasyonunu kolaylaştırarak önemli bir nokta haline gelmiştir. Bununla birlikte, entegrasyonları, teknik performans ve yasal uyumlulukla sınırlı olmayan uzun süredir devam eden etik sorunları da beraberinde getirmektedir. Bu çalışma, biyomedikal etik, profesyonel tıp standartları ve bilgi etiğinde sunulan bilgilerin sentezi yoluyla, sağlık hizmetlerinde veri madenciliğine ilişkin kavramsal temelli bir etik çerçeve sunmaktadır. Son literatürün bütünleyici nitel bir incelemesine dayanan analiz, algoritmik önyargı, hasta özerkliğinin aşınması, bilgilendirilmiş onamın sınırlılıkları, veri sahipliği sorunları ve kurumsal hesap verebilirlik açıkları gibi tekrarlayan etik sorunların belirlenebileceğini ortaya koymaktadır. Bu sonuçlara dayanarak, önerilen çerçeve, etik hususların temel direkleri olarak profesyonel sorumluluk, tasarım yoluyla şeffaflık, bağlamsal onam ve eşitlik odaklı denetimi belirlemektedir. Araştırma, veri odaklı yeniliği hasta merkezli değerler ve etik sağlık hizmeti sunumuyla uyumlu hale getirmek için klinisyenler, veri bilimciler ve politika yapıcılar tarafından kullanılması gereken profesyonel temelli bir yol haritası sunmaktadır.

Anahtar Kelimeler

• Veri Madenciliği
• Sağlık Etiği
• Algoritmik Önyargı
• Bilgilendirilmiş Onam
• Veri Sahipliği
• Mesleki Sorumluluk
• Tıbbi Yapay Zekâ

Destekleyen Kurum

International Standard University (ISU)

Etik Beyan

This research was conducted in accordance with ethical standards for scholarly inquiry. No human participants, personal data, or sensitive health information were involved. All sources have been properly cited, and the study adheres to principles of academic integrity, transparency, and responsible data use.

Teşekkür

The author gratefully acknowledges the support of International Standard University in facilitating this research. Appreciation is also extended to the editorial team and reviewers for their valuable insights and commitment to scholarly excellence.

Ethical Data Mining in Healthcare: Toward a Professionally Grounded Framework

Öz

The use of data mining technology in healthcare provides effective tools for resource management, therapy customization, and diagnosis. But there are also difficult ethical issues with their use. By critically analyzing important issues such algorithmic bias, patient autonomy, data ownership, and informed permission, this study offers a conceptual framework for ethical data mining in the healthcare industry. The framework places a strong emphasis on responsibility, transparency, and equity while building upon the moral obligations of healthcare workers and the fundamental ideas of bioethics. The purpose of this work is to assist physicians, data scientists, and policymakers in using data mining techniques in medical settings while making morally correct choices.

Anahtar Kelimeler

• data mining
• healthcare ethics
• algorithmic bias
• informed consent
• data ownership
• professional responsibility
• medical AI

Destekleyen Kurum

International Standard University (ISU)

Etik Beyan

This research was conducted in accordance with ethical standards for scholarly inquiry. No human participants, personal data, or sensitive health information were involved. All sources have been properly cited, and the study adheres to principles of academic integrity, transparency, and responsible data use.

Teşekkür

The author gratefully acknowledges the support of International Standard University in facilitating this research. Appreciation is also extended to the editorial team and reviewers for their valuable insights and commitment to scholarly excellence.

Kaynakça

1. Adeniyi, A. O., Arowoogun, J. O., Okolo, C. A., Chidi, R., & Babawarun, O. (2024). Ethical considerations in healthcare IT: A review of data privacy and patient consent issues. World Journal of Advanced Research and Reviews, 21(2), 1–10.
2. Ahmad, A., Mustafa, A., Qureshi, A. A., et al. (2024). Telemedicine practice: Current challenges of consent and autonomy, patient privacy, and data security worldwide. Journal of Social Preventive and Advanced Research KEMU.
3. Alqahtani, S., Ahmad, A. M., Alsharqi, O. Z., & Qalai, D. A. (2015). The impact of the code of medical ethics on health service quality among physicians at Saudi hospitals of Jeddah. American Academic & Scholarly Research Journal, 7, 30.
4. Baxi, H. D., & Sheth, M. (2020). Professionalism as a core value of postgraduate physiotherapy students of Ahmedabad: A cross-sectional survey. International Journal of Community Medicine and Public Health, 7(10), 4885.
5. Beauchamp, T. L., & Childress, J. F. (2019). Principles of biomedical ethics (8th ed.). Oxford University Press.
6. Bear Don’t Walk, O. J., Reyes Nieva, H., Lee, S., et al. (2022). A scoping review of ethics considerations in clinical natural language processing. JAMIA Open, 5.
7. Delgado, J., de Manuel, A., Parra, I., et al. (2022). Bias in algorithms of AI systems developed for COVID-19: A scoping review. Journal of Bioethical Inquiry, 19, 407–419.
8. Fino, L. B., Basheti, I., Saini, B., Moles, R., & Chaar, B. (2020). Exploring pharmacy ethics in developing countries: A scoping review. International Journal of Clinical Pharmacy, 42(3), 418–435.

9. Godongwana, M., Chewparsad, J., Lebina, L., Golub, J., Martinson, N., & Jarrett, B. (2021). Ethical implications of eHealth tools for delivering STI/HIV laboratory results and partner notifications. Current HIV/AIDS Reports, 18(5), 237–246.
10. Hutchings, E., Loomes, M. W., Butow, P., et al. (2021). A systematic literature review of attitudes towards secondary use and sharing of health administrative and clinical trial data: A focus on consent. Systematic Reviews, 10, 16.
11. Jackson, B. R., Kaplan, B., Schreiber, R., DeMuro, P. R., Nichols-Johnson, V., Ozeran, L., Solomonides, A., & Koppel, R. (2024). Ethical dimensions of clinical data sharing by U.S. healthcare organizations for purposes beyond direct patient care: Interviews with healthcare leaders. Applied Clinical Informatics, 15(2), 1–15.
12. Jonasson, L., Liss, P., Westerlind, B., & Berterö, C. (2011). Corroborating indicates nurses’ ethical values in a geriatric ward. International Journal of Qualitative Studies on Health and Well-being, 6, 7291.
13. Kaushik, P., Sharma, K., Mahawar, M., Wasim, J., Dey, G., & Nibiya, S. (2024). Ethical considerations in data mining and analytics. In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N) (pp. 1516–1521). IEEE.
14. Leslie, D., Mazumder, A., Peppin, A., Chan, B., & Hancock, B. (2021). Does AI stand for augmenting inequality in the era of Covid-19 healthcare? BMJ, 372, n304.
15. Maher, N. A., Senders, J., Hulsbergen, A., Lamba, N., Parker, M., Onnela, J., Bredenoord, A., Smith, T., & Broekman, M. (2019). Passive data collection and use in healthcare: A systematic review of ethical issues. International Journal of Medical Informatics, 129, 242–247.
16. McLennan, S., Strech, D., & Kahrass, H. (2022). The spectrum of ethical issues in data-intensive health research: An integrative review. BMC Medical Ethics, 23(1), 1–19.
17. Momotaj, M., Hasan, M., Sany, S. M. A., et al. (2024). Harnessing big data and machine learning for transformative healthcare information management. International Journal of Health and Medicine, 2024.
18. Nazeer, M. Y. (2024). Algorithmic conscience: An in-depth inquiry into ethical dilemmas in artificial intelligence. International Journal of Research and Innovation in Social Science (IJRISS), 8(5). https://doi.org/10.47772/IJRISS.2024.805052
19. 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.
20. Parate, A. P., Iyer, A. A., Gupta, K., et al. (2024). Review of data bias in healthcare applications. International Journal of Online and Biomedical Engineering, 20(12), 49997.
21. Perets, O., McNichol, M., Stagno, L., et al. (2024). Inherent bias in electronic health records: A scoping review of sources of bias. medRxiv.
22. Rahman, H. (2024). Securing data privacy in blockchain networks. In Blockchain technology applications in knowledge management (pp. 367–411). IGI Global Scientific Publishing.
23. Rahman, H. (2025). The ethical dimensions of digital interactions. In Digital citizenship and building a responsible online presence (pp. 123–164). IGI Global Scientific Publishing.
24. Rubeis, G. (2022). iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interventions, 28, 100518.
25. Saelaert, M., Mertes, H., Moerenhout, T., de Baere, E., & Devisch, I. (2020). Ethical values supporting the disclosure of incidental and secondary findings in clinical genomic testing: A qualitative study. BMC Medical Ethics, 21(1), 45.
26. Shabani, M., Bezuidenhout, L., & Borry, P. (2018). Attitudes of research ethics committee members toward the return of individual research results and incidental findings in genomic research. European Journal of Human Genetics, 26(3), 306–313.
27. Tipton, K., Leas, B., Flores, E., et al. (2023). Impact of healthcare algorithms on racial and ethnic disparities in health and healthcare. Journal of Health Equity, 2023.
28. Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689.