This is a preview and has not been published. View submission

HealthChain: A Hybrid Blockchain for Scalable and Secure Healthcare Data Management

Authors

  • Ahmad Mousa Altamimi Princess Sumaya University for Technology, Amman, Jordan
  • Lamia Al-Kershi Princess Sumaya University for Technology, Amman, Jordan
  • Qusay Abdo Princess Sumaya University for Technology, Amman, Jordan
Volume: 16 | Issue: 3 | Pages: 36438-36446 | June 2026 | https://doi.org/10.48084/etasr.17941

Abstract

Blockchain technology has emerged as a promising foundation for secure, transparent, and patient-centric healthcare data management; however, the scalability, latency, and data storage limitations of conventional blockchains hinder their adoption in data-intensive healthcare environments involving Electronic Health Records (EHRs), medical imaging, and continuous sensor data streams. To address these limitations, sidechain architectures have been proposed as a viable solution, enabling off-chain processing and storage while maintaining cryptographic anchoring to a secure main blockchain. In this context, the paper introduces HealthChain, a formally specified sidechain-based architecture for scalable, privacy-preserving healthcare data management. Unlike prior work that remains largely conceptual, HealthChain provides an explicit definition of system components, cross-chain interaction mechanisms, data management policies, and governance responsibilities. Furthermore, the proposed architecture separates governance and auditability functions, maintained on a permissioned main blockchain, from high-throughput healthcare data processing, delegated to domain-specific sidechains. This design enables efficient handling of large-scale healthcare data while preserving security and compliance requirements. The proposed model is evaluated using blockchain-relevant performance metrics, including transaction throughput, latency, storage overhead, and cross-chain communication cost. Comparative analysis demonstrates that HealthChain significantly improves scalability and efficiency over a traditional main-chain-only approach, while preserving decentralization, data integrity, and regulatory compliance. Overall, the results highlight the potential of sidechain-based architectures to enable practical, large-scale deployment of blockchain technologies in healthcare systems.

Keywords:

blockchain, sidechains, healthcare data management, scalability, privacy, interoperability

Downloads

Download data is not yet available.

References

F. M. AbdelSalam, "Blockchain Revolutionizing Healthcare Industry: A Systematic Review of Blockchain Technology Benefits and Threats," Perspectives in Health Information Management, vol. 20, no. 3, Sep. 2023, Art. no. 1b.

M. S. B. Kasyapa and C. Vanmathi, "Blockchain integration in healthcare: a comprehensive investigation of use cases, performance issues, and mitigation strategies," Frontiers in Digital Health, vol. 6, Apr. 2024, Art. no. 1359858.

S. Felemban et al., "Current application of blockchain technology in healthcare and its potential roles in Urology," BJU International, vol. 136, pp. S5-S17, Oct. 2025.

H. Taherdoost, "Privacy and Security of Blockchain in Healthcare: Applications, Challenges, and Future Perspectives," Sci, vol. 5, no. 4, Oct. 2023, Art. no. 41.

V. Sitharamulu, G. Sucharitha, S. Nandan Mohanty, S. Janbhasha, and D. Kothandaraman, "A private Ethereum blockchain for organ donation and transplantation based on intelligent smart contracts," Egyptian Informatics Journal, vol. 28, Dec. 2024, Art. no. 100542.

J. Werth, M. Berenjestanaki, H. Barzegar, N. El Ioini, and C. Pahl, "A Review of Blockchain Platforms Based on the Scalability, Security and Decentralization Trilemma:," in Proceedings of the 25th International Conference on Enterprise Information Systems, 2023, pp. 146–155.

M. K. Pawar, P. Patil, and P. S. Hiremath, "A Study on Blockchain Scalability," in ICT Systems and Sustainability, vol. 1270, M. Tuba, S. Akashe, and A. Joshi, Eds. Singapore: Springer Singapore, 2021, pp. 307–316.

F. Hashim, K. Shuaib, and N. Zaki, "Sharding for Scalable Blockchain Networks," SN Computer Science, vol. 4, no. 1, Oct. 2022, Art. no. 2.

Y. Liu et al., "Building blocks of sharding blockchain systems: Concepts, approaches, and open problems," Computer Science Review, vol. 46, Nov. 2022, Art. no. 100513.

A. Singh, K. Click, R. M. Parizi, Q. Zhang, A. Dehghantanha, and K.-K. R. Choo, "Sidechain technologies in blockchain networks: An examination and state-of-the-art review," Journal of Network and Computer Applications, vol. 149, Jan. 2020, Art. no. 102471.

R. Deepa and M. S. Arya, "Blockchain-Sidechain Based Data Storage for Reimaging Electronic Health Record via Optimized Interplanetary File System," in Information and Communication Technology for Competitive Strategies (ICTCS 2020), vol. 191, A. Joshi, M. Mahmud, R. G. Ragel, and N. V. Thakur, Eds. Singapore: Springer Singapore, 2022, pp. 1097–1110.

A. S. Yadav, N. Singh, and D. S. Kushwaha, "Sidechain: storage land registry data using blockchain improve performance of search records," Cluster Computing, vol. 25, no. 2, pp. 1475–1495, Apr. 2022.

O. Kuznetsov, P. Sernani, L. Romeo, E. Frontoni, and A. Mancini, "On the Integration of Artificial Intelligence and Blockchain Technology: A Perspective About Security," IEEE Access, vol. 12, pp. 3881–3897, 2024.

D. Bhumichai, C. Smiliotopoulos, R. Benton, G. Kambourakis, and D. Damopoulos, "The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead," Information, vol. 15, no. 5, May 2024, Art. no. 268.

Monika and R. Bhatia, "Interoperability Solutions for Blockchain," in 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Oct. 2020, pp. 381–385.

I. Yaqoob, K. Salah, R. Jayaraman, and Y. Al-Hammadi, "Blockchain for healthcare data management: opportunities, challenges, and future recommendations," Neural Computing and Applications, vol. 34, no. 14, pp. 11475–11490, July 2022.

N. Atzei, M. Bartoletti, and T. Cimoli, "A Survey of Attacks on Ethereum Smart Contracts (SoK)," in Principles of Security and Trust, vol. 10204, M. Maffei and M. Ryan, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017, pp. 164–186.

K. Divya. and M. Mohan, "Sidechain: A Scalable Blockchain," in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), May 2022, pp. 1337–1342.

M. Li, H. Tang, A. R. Hussein, and X. Wang, "A Sidechain-Based Decentralized Authentication Scheme via Optimized Two-Way Peg Protocol for Smart Community," IEEE Open Journal of the Communications Society, vol. 1, pp. 282–292, 2020.

H. Wang and R. Zhou, "The Application of Blockchain to Electronic Health Record Systems: A Review," in 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), Dec. 2021, pp. 397–401.

H. S. Adams. "NTT & Olympus: World's First Cloud Endoscopy System." Healthcare Digital. [Online]. Available: https://healthcare-digital.com/technology-and-ai/ntt-olympus-worlds-first-cloud-endoscopy-system.

B. Chavali, S. K. Khatri, and S. A. Hossain, "AI and Blockchain Integration," in 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), June 2020, pp. 548–552.

S. S. Mohammed Abdul, "Navigating Blockchain’s Twin Challenges: Scalability and Regulatory Compliance," Blockchains, vol. 2, no. 3, pp. 265–298, July 2024.

A. Qambar, K. Shuaib, and M. Gergely, "Governing Blockchains in the Healthcare Ecosystem," in Blockchain for Biomedical Research and Healthcare, P. Kumar and A. Kumari, Eds. Singapore: Springer Nature Singapore, 2024, pp. 145–170.

J. R. Quinlan, "Learning decision tree classifiers," ACM Computing Surveys, vol. 28, no. 1, pp. 71–72, Mar. 1996.

L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001.

L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: unbiased boosting with categorical features," in Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, 2018, pp. 6639–6649.

T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California, USA, Aug. 2016, pp. 785–794.

T. G. Dietterich, "Ensemble Methods in Machine Learning," in Multiple Classifier Systems, vol. 1857, Berlin, Heidelberg: Springer Berlin Heidelberg, 2000, pp. 1–15.

D. M. W. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," 2020.

Medical Students Dataset. (2023), S. Salem. [Online]. Available: https://www.kaggle.com/datasets/slmsshk/medical-students-dataset/data.

J. C. Mandel, D. A. Kreda, K. D. Mandl, I. S. Kohane, and R. B. Ramoni, "SMART on FHIR: a standards-based, interoperable apps platform for electronic health records," Journal of the American Medical Informatics Association, vol. 23, no. 5, pp. 899–908, Sept. 2016.

A. S. Alfakeeh, "A Blockchain-Enabled IoT Framework for Secure Attack Detection and Advanced Feature Selection in Smart Healthcare," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 28219–28223, Oct. 2025.

Downloads

How to Cite

[1]
A. M. Altamimi, L. Al-Kershi, and Q. Abdo, “HealthChain: A Hybrid Blockchain for Scalable and Secure Healthcare Data Management”, Eng. Technol. Appl. Sci. Res., vol. 16, no. 3, pp. 36438–36446, Jun. 2026.

Metrics

Abstract Views: 4
PDF Downloads: 3

Metrics Information