Sağlık Kuruluşlarında Karar Zekâsı: İnsan Uzmanlığı ile Yapay Zekâyı Bütünleştiren Bir Yönetişim Çerçevesi

Abstract

Sağlık kuruluşları, yapay zekâyı klinik ve yönetsel karar süreçlerine bu süreçleri yönetecek yönetişim kapasitesini geliştirdiklerinden daha hızlı entegre etmektedir. Bu çalışma, insan uzmanları ile algoritmik sistemlerin birlikte oluşturduğu kararların tahsisi, doğrulanması ve hesap verebilirliğini açıklayan bir yönetişim yetkinliği olarak karar zekâsı kavramını geliştirmektedir. Sınırlı rasyonellik kuramı, profesyonel uzmanlık sosyolojisi ve sağlıkta yapay zekâ yönetişimi literatüründen yararlanılarak geliştirilen kavramsal çerçeve; epistemik tahsis, yerel doğrulama ve hesap verebilirlik bütünleştirmesi olmak üzere üç temel yönetişim mekanizması önermektedir. Çerçeve, Duke Health, MD Anderson Cancer Center ve Epic Sepsis Modeli vakalarının karşılaştırmalı analiziyle temellendirilmektedir. Çalışma, sağlık kuruluşlarında yapay zekâ yönetişimini teknoloji odaklı yaklaşımdan karar odaklı bir bakış açısına taşıyarak sağlık yönetimi uygulamaları ve gelecekteki araştırmalar için kavramsal ve yönetsel katkılar sunmaktadır.

Keywords

• karar zekâsı
• yapay zekâ
• sağlık yönetişimi
• klinik karar desteği
• hesap verebilirlik

Supporting Institution

Bu çalışma herhangi bir kamu kurumu, özel kuruluş veya fon sağlayıcı tarafından desteklenmemiştir.

Project Number

015

Ethical Statement

Bu çalışma yalnızca yayımlanmış bilimsel literatüre dayalı bir derleme makalesidir. Çalışmada insan katılımcılar, hasta verileri, deney hayvanları veya kişisel veri kullanılmadığından etik kurul onayı gerekmemektedir.

Thanks

Yazarlar, makalenin değerlendirme sürecine ayırdığı zaman ve gösterdiği özen için Editöre teşekkür eder.

Decision Intelligence in Healthcare Organizations: A Governance Framework Integrating Human Expertise and Artificial Intelligence

Abstract

Healthcare organizations are adopting artificial intelligence into clinical and managerial decision-making faster than they are developing the governance capability to manage it, and the consequences of that asymmetry are increasingly evident. This review develops decision intelligence as a governance construct describing the organizational capability to allocate, validate, and account for decisions made jointly by human experts and algorithmic systems. Drawing on bounded rationality, the sociology of professional expertise, and the emerging literature on artificial intelligence governance in healthcare, the paper argues that the key governance challenge is not whether algorithms outperform clinicians, but how healthcare organizations determine which decisions should be delegated, how algorithmic performance should be validated within local clinical settings, and how accountability should be maintained in hybrid decision processes. The proposed framework consists of three governance mechanisms: epistemic allocation, local validation, and accountability integration. The framework is illustrated through a structured comparison of documented cases from Duke Health, MD Anderson Cancer Center, and the Epic Sepsis Model. The paper contributes a diagnostic governance framework that shifts the focus of healthcare AI governance from technology to the decision itself and provides practical implications for healthcare management, organizational governance, and future research.

Keywords

• decision intelligence
• artificial intelligence
• healthcare governance
• clinical decision support
• accountability

Supporting Institution

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Project Number

015

Ethical Statement

This study is a review article based exclusively on previously published scientific literature. No human participants, patient data, animal subjects, or identifiable personal data were involved. Therefore, ethics committee approval was not required.

Thanks

The authors would like to thank the Editor for managing the editorial process and for the valuable time and effort devoted to the evaluation of this manuscript.

References

1. Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O'Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., Huang, E., Poon, E. G., & Pencina, M. J. (2022). A framework for the oversight and local deployment of safe and high-quality prediction models. Journal of the American Medical Informatics Association, 29(9), 1631–1636. https://doi.org/10.1093/jamia/ocac078
2. Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. npj Digital Medicine, 3, 118. https://doi.org/10.1038/ s41746-020-00324-0
3. Freidson, E. (2001). Professionalism: The third logic. Polity Press.
4. Habib, A. R., Lin, A. L., & Grant, R. W. (2021). The Epic Sepsis Model falls short: The importance of external validation. JAMA Internal Medicine, 181(8), 1040–1041. https://doi.org/10.1001/jamainternmed.2021.3333
5. Muehlematter, U. J., Daniore, P., & Vokinger, K. N. (2021). Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): A comparative analysis. The Lancet Digital Health, 3(3), e195–e203. https://doi.org/10.1016/S2589-7500(20)30292-2
6. Muehlematter, U. J., Bluethgen, C., & Vokinger, K. N. (2023). FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks. The Lancet Digital Health, 5(9), e618– e626. https://doi.org/10.1016/S2589-7500(23)00126-7
7. Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491–497. https://doi.org/10.1093/jamia/ocz192
8. Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852

9. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
10. U.S. Food and Drug Administration. (2026). Artificial intelligence and machine learning (AI/ML)-enabled medical devices. https://www.fda.gov/medicaldevices/ software-medical-device-samd/artificial-intelligence-enabledmedical- devices
11. University of Texas System Audit Office. (2016). Special review of procurement procedures related to the M.D. Anderson Cancer Center Oncology Expert Advisor project. https://www.utsystem.edu
12. Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer- Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., Ghous, M., & Singh, K. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065–1070. https://doi.org/10.1001/jamainternmed.2021.2626
13. Wu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., & Zou, J. (2021). How medical AI devices are evaluated: Limitations and recommendations from an analysis of FDA approvals. Nature Medicine, 27(4), 582–584. https://doi.org/10.1038/s41591-021-01312-x