STAP Journal of Security Risk Management

ISSN: 3080-9444 (Online)

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Article

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Securing Healthcare Digital Twin with Blockchain: A Systematic Review of Architecture, Threats and Evaluation

by 

Dawood Alalisalem ;

Hafizur Rahman

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Published: 2026

Abstract

Recently, it has been noted that the convergence of blockchain technology presents a promising paradigm for secure, privacy-preserving, and transparent healthcare systems. Moreover, Digital Twins enable real-time replication of patients, hospital operations, and medical devices, and their dependence on continuous sensitive data streams introduces the latest trust and Cybersecurity challenges. A systematic literature review aims to investigate how distributed ledger and blockchain technologies have been applied to secure healthcare digital twins from 2020 to 2025. Furthermore, the review addresses the proposed architecture of blockchain, the security objectives targeted, integration approaches within digital twins, and evaluation methods with limitations. The study follows PRISMA 2020 guidelines. Web of Sciences, IEEE Xplore, PubMed, Scopus, and ACM Digital Library were searched from January 2020 to October 2025 by using defined Boolean queries. Also, the focus of the inclusion criteria is on peer-reviewed studies that discussed blockchain for DT security in healthcare. Data extraction captured blockchain type, metadata, security mechanisms, DT domain, and evaluation methods. From the 487 identified records, only 20 successfully met the inclusion criteria. The fact behind it is that most studies only employed permissioned blockchains like Quorum and Hyperledger integrated with digital twins for monitoring patients, device lifecycle tracking, and data provenance. Some main security objectives include provenance assurance, access control, and integrity. Moreover, only some studies provide formal threat analysis or real-world deployment. Blockchain technology is reliable because it increases digital twin security through immutability, smart-contract-based governance, and decentralized trust. However, interoperability, scalability, and privacy-preserving computation remain the main barriers for clinical adoption.

Keywords

PRISMAHealthcare securityBlockchainDigital twinData integrityIoMTSystematic review

How to Cite the Article

Alalisalem, D., & Rahman, H. (2026). Securing Healthcare Digital Twin with Blockchain: A Systematic Review of Architecture, Threats and Evaluation. STAP Journal of Security Risk Management, 2026(1), 46–66. https://doi.org/10.63180/jsrm.thestap.2026.1.3

References

  1. Amofa, S., Xiao, H. X. I. A. O. B. A.-A., J. Y., & Qi, X. (2024). Blockchain-secure patient digital twin in healthcare using smart contracts. PLOS ONE, 19(2), e0286120.
  2. Suleiman, R., Y., W., & Akshita Maradapu Vera Venkata Sai, C. W. (2025). Blockchain for security in digital twins. Future Internet, 17(9), 385.
  3. Dai, Y., X., S. M., B. G. Y. Q., L., Y., & Wu, J. (2024). Blockchain empowered access control for digital twin system with attributebased encryption. Future Generation Computer Systems, 160, 564–576.
  4. Alghareeb, M. S., & Almaayah, M. (2025). Cyber Security Risk Management for Threats in Wireless LAN: A Literature Review. STAP Journal of Security Risk Management, 2025(1), 22-58.
  5. Machado, T. M., & Berssaneti, F. T. (2023). Literature review of digital twin in healthcare. Heliyon, 9(9).
  6. Guo, Z.-Z., Q., J.-Y., L., X.-B., R., D.-C., S., Y., & Wang, K. (2021). An intelligent maritime scene frame prediction based on digital twins technology. In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) (pp. 25–28). IEEE.
  7. Suhail, S., O., R. J. A., S. C. S. H., & Hussain, R. M. (2022). Blockchain-based digital twins: Research trends, issues, and future challenges. ACM Computing Surveys, 54(11s), 1–34.
  8. Vallée, A. (2023). Digital twin for healthcare systems. Frontiers in Digital Health, 5, 1253050.
  9. Shankhdhar, A., & Garg, H. (2025). Blockchain-enabled secure data transmission for personalized e-healthcare and digital twin wellbeing. Cluster Computing, 28(15).
  10. Gupta, M., Jain, R., Kumari, M., & Narula, G. (2020). Securing healthcare data by using blockchain. In Applications of Blockchain in Healthcare (pp. 93–114).
  11. Shinde, R., Patil, S., Kotecha, K., Potdar, V., Selvachandran, G., & Abraham, A. (2024). Securing AI-based healthcare systems using blockchain technology: A state-of-the-art systematic literature review and future research directions. Transactions on Emerging Telecommunications Technologies, 35(1).
  12. Zarour, M., Ansari, M. T. J., Alenezi, M., Sarkar, A. K., Faizan, M., Agrawal, A., Kumar, R., & Khan, R. A. (2020). Evaluating the impact of blockchain models for secure and trustworthy electronic healthcare records. IEEE Access, 8.
  13. Al-shareeda, M., & Alrudainy, H. (2026). Sustainable and Secure Energy Optimization Strategies in the Internet of Healthcare Things (IoHT). International Journal of Cybersecurity Engineering and Innovation, 2026(1).
  14. Ghosh, P. K., R., M. H. K., S., A. H., & Chakraborty, A. (2023). Blockchain application in healthcare systems: A review. Systems, 11(1), 38.
  15. Alrajeh, M., Almaiah, M., & Mamodiya, U. (2026). Cyber Risk Analysis and Security Practices in Industrial Manufacturing: Empirical Evidence and Literature Insights. International Journal of Cybersecurity Engineering and Innovation, 2026(1).
  16. Ibrahim, A., Kadhim, A. F., Hamzah, A. E., & Al-Shareeda, M. A. (2026). A Secure and Scalable IoT Home Automation Architecture with Web and Biometric Control. International Journal of Cybersecurity Engineering and Innovation, 2026(1).
  17. Wells, K., & Littell, J. H. (2009). Study quality assessment in systematic reviews of research on intervention effects. Research on Social Work Practice, 19(1), 52–62.
  18. Sterne, J. A., Hernán, M. A., McAleenan, A., Reeves, B. C., & Higgins, J. P. (2019). Assessing risk of bias in a non-randomized study. In Cochrane Handbook for Systematic Reviews of Interventions (pp. 621–641).
  19. Whiting, P., Savović, J., & Higgins, J. P. (2016). ROBIS: A new tool to assess risk of bias in systematic reviews was developed. Journal of Clinical Epidemiology, 69, 225–234.
  20. Lundh, A., & Gøtzsche, P. C. (2008). Recommendations by Cochrane Review Groups for assessment of the risk of bias in studies. BMC Medical Research Methodology, 8(1).
  21. Talib, A. H., AL-Nakkash, A. H., Wadday, A. G., Abed, A. A., & Al-Shareeda, M. A. (2026). Real-Time Spectrum Sensing on an RTL-SDR-Based IoT Platform. International Journal of Cybersecurity Engineering and Innovation, 2026(1).
  22. Yaqoob, I., J., M. U. R., O., M. I., & Salah, K. (2020). Blockchain for digital twins: Recent advances and future research challenges. IEEE Network, 34(5), 290–298.
  23. Suhail, S., & Hussain, R. J. a. C. S. H. (2021). Trustworthy digital twins in the industrial internet of things with blockchain. IEEE Internet Computing, 26(3), 58–67.
  24. Zheng, Q., S., Y. P. D., C., M., & Wang, J. (2022). Blockchain-based trustworthy digital twin in the Internet of Things. In 2022 International Conference on Information Processing and Network Provisioning (ICIPNP) (pp. 152–155). IEEE.
  25. Xames, M. D., & Topcu, T. G. (2024). A systematic literature review of digital twin research for healthcare systems: Research trends, gaps, and realization challenges. IEEE Access, 12, 4099–4126.