Digital Twin Hospitals: Building Algorithmic Replicas for Predictive Healthcare Governance

Öz

Decision-making in contemporary healthcare systems has become increasingly complex as a result of persistent resource constraints, growing uncertainty in service demand, heterogeneous and high-risk patient profiles, and multi-layered governance structures involving diverse stakeholders. While the adoption of artificial intelligence, big data analytics, and advanced decision support systems in hospital management has expanded significantly, the literature remains fragmented regarding how these technologies can be integrated into a comprehensive, simulation-based governance framework. In particular, the strategic governance role of the Digital Twin Hospital concept enabled through the real-time integration of financial, clinical, patient, risk, and human resource data has received limited systematic attention. This study adopts a conceptual narrative review methodology and synthesizes international literature spanning digital twin technologies, AI-driven decision support systems, hospital governance models, and simulation-based management approaches. Building on this synthesis, the paper argues that digital twin hospitals should be understood not merely as tools for operational optimization or efficiency improvement, but as strategic governance infrastructures that enable predictive, anticipatory, and evidence-informed decision-making. By facilitating real-time scenario modeling and “what-if” simulations, digital twin hospitals enhance managerial foresight, improve decision quality under conditions of uncertainty, and reduce institutional dependence on reactive and retrospective management practices. The study contributes to the digital health governance literature by reframing digital twin hospitals as foresight-oriented platforms capable of supporting resilient, adaptive, and data-integrated healthcare governance.

Anahtar Kelimeler

• Artificial intelligence
• Healthcare
• Hospital Administration

Dijital İkiz Hastaneler: Öngörücü Sağlık Yönetişimi için Algoritmik Replikaların İnşası

Öz

Günümüz sağlık sistemlerinde karar alma süreçleri; kalıcı kaynak kısıtları, hizmet talebindeki sürekli dalgalanmalar, giderek karmaşıklaşan ve çoklu riskler barındıran hasta profilleri ile çok paydaşlı ve çok katmanlı yönetişim yapıları nedeniyle önemli ölçüde karmaşık hâle gelmiştir. Hastane yönetiminde yapay zekâ, büyük veri analitiği ve gelişmiş karar destek sistemlerinin kullanımına ilişkin literatür son yıllarda hızlı bir genişleme göstermiş olsa da, bu teknolojilerin bütüncül ve simülasyon temelli bir yönetişim çerçevesi içinde nasıl konumlandırılacağı hâlen sınırlı düzeyde ele alınmaktadır. Özellikle finansal, klinik, hasta, risk ve insan kaynağı verilerinin eş zamanlı ve bütünleşik biçimde kullanılması yoluyla öngörücü ve proaktif karar alma süreçlerini destekleyen Dijital İkiz Hastane yaklaşımının stratejik yönetişim kapasitesi literatürde yeterince tartışılmamıştır. Bu çalışma, dijital ikiz teknolojileri, yapay zekâ temelli karar destek sistemleri, hastane yönetişimi ve simülasyon tabanlı yönetim yaklaşımlarına ilişkin uluslararası literatürü kapsayan kavramsal bir anlatı derlemesi olarak tasarlanmıştır. Çalışmada, mevcut yaklaşımlar sistematik bir bakış açısıyla sentezlenmiş ve dijital ikiz hastanelerin sağlık kurumlarında nasıl bir yönetsel değer üretebileceği kavramsal düzeyde tartışılmıştır. Elde edilen bulgular, dijital ikiz hastanelerin yalnızca operasyonel verimliliği artırmaya yönelik teknik çözümler olarak değil; belirsizlik altında karar verme kapasitesini güçlendiren, yönetsel öngörüyü geliştiren, senaryo temelli analizleri mümkün kılan ve reaktif yönetim anlayışlarının sınırlılıklarını azaltan stratejik yönetişim platformları olarak ele alınması gerektiğini ortaya koymaktadır. Bu bağlamda çalışma, dijital ikiz hastaneleri öngörü temelli, uyarlanabilir ve dayanıklı sağlık yönetişimi altyapıları olarak yeniden kavramsallaştırarak dijital sağlık ve hastane yönetişimi literatürüne özgün bir katkı sunmaktadır.

Anahtar Kelimeler

• Yapay zeka
• Hastane Yönetimi
• Sağlık Hizmetleri

Kaynakça

1. Aloqaily, M., Bouachir, O., Karray, F., Al Ridhawi, I., & El Saddik, A. (2022). Integrating digital twin and advanced intelligent technologies to realize the metaverse. IEEE Consumer Electronics Magazine, 12(6), 47-55. https://doi.org/10.1109/MCE.2022.3212570
2. Boretti, A. (2025). Designing inclusive digital twins: ethical and practical considerations for trans healthcare. Journal of medical ethics, 51(12), 803-804. https://doi.org/10.1136/jme-2025-111120
3. Bruynseels, K., et al. (2018). Digital twins in health care: Ethical implications of an emerging engineering paradigm. Frontiers in Genetics, 9, 31. https://doi.org/10.3389/fgene.2018.00031
4. Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. McKinsey Global Institute, 1(2018), 3-84.
5. Centers for Medicare & Medicaid Services. (2024). Value-based care models. https://www.cms.gov/priorities/innovation/models
6. Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., ... & Lamata, P. (2020). The ‘Digital Twin’to enable the vision of precision cardiology. European heart journal, 41(48), 4556-4564. https://doi.org/10.1093/eurheartj/ehaa159
7. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
8. Drummond, M. F., et al. (2023). Methods for the economic evaluation of health care programmes (5th ed.). Oxford University Press.

9. European Commission. (2024). AI Act: High-risk systems in healthcare. https://digital-strategy.ec.europa.eu/en/policies/ai-act
10. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5
11. Friedman, B., Kahn Jr, P. H., Borning, A., & Huldtgren, A. (2013). Value sensitive design and information systems. In Early engagement and new technologies: Opening up the laboratory (pp. 55-95). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-7844-3_4
12. Garcez, A. D. A., & Lamb, L. C. (2023). Neurosymbolic ai: The 3 rd wave. Artificial Intelligence Review, 56(11), 12387-12406. https://doi.org/10.1007/s10462-023-10448-w
13. Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems (pp. 85–113). Springer.
14. Gul, M., & Guneri, A. F. (2015). A comprehensive review of emergency department simulation applications. Health Care Management Science, 18(3), 408–432. https://doi.org/10.1016/j.cie.2015.02.018
15. IEEE. (2025). Digital twins in healthcare: Ethical and technical frontiers. IEEE Standards Association.
16. Jin, M., & Lee, H. (2025). Position: Ai safety must embrace an antifragile perspective. arXiv preprint arXiv:2509.13339. https://doi.org/10.48550/arXiv.2509.13339
17. Junga, A., Schulze, H., Scherzer, S., Hätscher, O., Bozdere, P., Schmidle, P., ... & medical tr. AI. ning consortium. (2024). Immersive learning in medical education: analyzing behavioral insights to shape the future of VR-based courses. BMC Medical Education, 24(1), 1413. https://doi.org/10.1186/s12909-024-06337-7
18. Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021). Physics-informed machine learning: Nature Reviews Physics.(2021).
19. Lv, Z., Qiao, L., Mardani, A., & Lv, H. (2022). Digital twins on the resilience of supply chain under COVID-19 pandemic. IEEE Transactions on Engineering Management, 71, 10522-10533. . https://doi.org/10.1109/TEM.2022.3195903
20. Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. Mehrabi, N., et al. (2021). A survey on bias in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
21. Miles, I., & Keenan, M. (2002). Practical foresight guide. PREST.
22. Mordor Intelligence. (2025). Digital twin market in healthcare - growth, trends, 2025-2030. https://www.mordorintelligence.com/industry-reports/digital-twin-market-healthcare
23. National Academies of Sciences, Engineering, and Medicine. (2024). Digital twins: A new paradigm for health systems. National Academies Press. https://doi.org/10.17226/27435
24. 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
25. Osborne, M. J. (2004). An introduction to game theory. Oxford University Press.
26. Oulefki, A., Amira, A., & Foufou, S. (2025). Digital twins and AI transforming healthcare systems through innovation and data-driven decision making. Health and Technology, 1-23. https://doi.org/10.1007/s12553-025-00947-x
27. Pellegrino, G., Gervasi, M., Angelelli, M., & Corallo, A. (2025). A conceptual framework for digital twin in healthcare: Evidence from a systematic meta-review. Information Systems Frontiers, 27(1), 7-32. https://doi.org/10.1007/s10796-024-10536-4
28. Rajkomar, A., et al. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 18. https://doi.org/10.1038/s41746-018-0029-1
29. Rasheed, A., et al. (2020). Digital twin in industry: A review. CIRP Journal of Manufacturing Science and Technology, 29, 59–74. https://doi.org/10.1016/j.cirpj.2020.02.002
30. Ribeiro, M. T., et al. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778
31. Rubinstein, R. Y., & Kroese, D. P. (2008). Simulation and the Monte Carlo method (2nd ed.). Wiley. Sadée, C., Testa, S., Barba, T., Hartmann, K., Schuessler, M., Thieme, A., ... & Gevaert, O. (2025). Medical digital twins: enabling precision medicine and medical artificial intelligence. The Lancet Digital Health.
32. Subagdja, B., Shanthoshigaa, D., Wang, Z., & Tan, A. H. (2024). Machine learning for refining knowledge graphs: A survey. ACM Computing Surveys, 56(6), 1-38. https://doi.org/10.1145/3640313
33. Subbiah, V. (2023). The next generation of evidence-based medicine. Nature medicine, 29(1), 49-58. https://doi.org/10.1038/s41591-022-02160-z
34. Sun, T., He, X., & Li, Z. (2023). Digital twin in healthcare: Recent updates and challenges. Digital health, 9, 20552076221149651. https://doi.org/10.1177/20552076221149651
35. Tao, F., et al. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
36. Tausch, N., & Zumbuehl, A. (2025). Consensus in AI-moderated boards. Management Science, 71(1), 450–465. https://doi.org/10.1287/mnsc.2023.4567
37. TechUK. (2025). AI Act implications for health tech. https://www.techuk.org/insights/ai-act-healthtech.html
38. 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
39. Yang, H., Jiang, G., Tian, W., Mei, X., Nee, A. Y. C., & Ong, S. K. (2025). Microservice-based digital twin system towards smart manufacturing. Robotics and Computer-Integrated Manufacturing, 91, 102858. https://doi.org/10.1016/j.rcim.2024.102858
40. Uhlemann, T. H.-J., et al. (2017). The digital twin: Demonstrating the potential. Procedia CIRP, 61, 55–60. https://doi.org/10.1016/j.procir.2017.03.054
41. Qu, X., Ding, X., Xu, Y. L., & Yu, W. (2023). Real-time outlier detection in integrated GNSS and accelerometer structural health monitoring systems based on a robust multi-rate Kalman filter. Journal of Geodesy, 97(4), 38. https://doi.org/10.1007/s00190-023-01724-2
42. Vesely, W. E., et al. (2020). Fault tree handbook. U.S. Nuclear Regulatory Commission.
43. Vig, J., et al. (2019). Investigating human explanations for model interpretability. arXiv preprint arXiv:1907.09696.
44. Weick, K. E., & Sutcliffe, K. M. (2015). Managing the unexpected: Sustained performance in a complex world (3rd ed.). Jossey-Bass.
45. World Economic Forum. (2023). The future of health: Digital twins. https://www.weforum.org/reports/future-of-health-digital-twins
46. World Health Organization. (2021). Global strategy on digital health 2020-2025. WHO Press.
47. World Health Organization. (2023). Health workforce report 2023. WHO Press.
48. World Health Organization. (2024). History as a partner in public health: a report of the foresight think tank on the history of pandemics. World Health Organization.