As digital healthcare continues to evolve, there is a growing concern about the secure sharing and privacy protection of healthcare data. Nowadays, although electronic health records (EHR) have become the main way for healthcare organisations to manage their data, most hospitals and clinics are still using their own centralised systems - in this way, not only is the data like ‘an island’ unconnected to each other, making it difficult for different organisations to collaborate with each other, but such systems are also vulnerable to hacking, leading to the leakage of patient information and compromising privacy rights. This not only makes the data like ‘islands’ that are not connected to each other, making it difficult for different organisations to collaborate with each other, but also makes such systems vulnerable to hacking, leading to the leakage of patient information and compromising everyone's privacy. Because of this, this paper explores the potential of blockchain technology in building a trustworthy medical data sharing mechanism, and also proposes a solution that protects privacy without affecting the operational effectiveness of the system. In this study, for the characteristics of medical scenarios, the coalition chain with access control is chosen as the underlying architecture, and the traditional consensus mechanism is improved directionally to design a hybrid consensus algorithm that fits the needs of the medical industry. This algorithm combines the proof-of-interest and probabilistic verification mechanisms, which can fairly distribute the node bookkeeping rights, significantly improve the system processing speed and enhance the anti-attack ability. For the possible node monopoly and voting imbalance in the consensus election, we introduce the multi-source random sequence generation method and ring signature technology, which makes the system more secure and stable in the complex network environment, and the election more transparent. Finally, considering the sensitivity of medical data, we have specially designed a dual protection mechanism of ‘layered processing’ and ‘cryptographic hashing’. Specifically, the patient's identity information and medical treatment data are stored separately, and also use SHA-256, an irreversible algorithm, to generate a unique identity information ‘identity fingerprint’. In this way, even if part of the data is accidentally leaked, it is difficult to restore the complete privacy information, thus making the system more secure in protecting the anonymity of the user and preventing the leakage of information by association. Comprehensive evaluation results demonstrate that the proposed blockchain-based medical data-sharing model achieves strong performance in efficiency, security, and privacy protection, thereby providing technical support for compliant, efficient, and trustworthy cross-institutional data collaboration.

Thanh-Huyen Truong. Research on the Application of Blockchain Technology in the Security of Digital Healthcare Data. International Journal of Health and Pharmaceutical Medicine (2025), Vol. 5, Issue 1: 32-42. https://doi.org/10.38007/IJHPM.2025.050104.

[1] Tertulino R, Antunes N, Morais H. Privacy in electronic health records: a systematic mapping study[J]. Journal of Public Health, 2024, 32(3): 435-454.

[2] Szarfman A, Levine J G, Tonning J M, et al. Recommendations for achieving interoperable and shareable medical data in the USA[J]. Communications medicine, 2022, 2(1): 86.

[3] Fan Y. Automatic Optimization of Trading Strategies Based on Reinforcement Learning[C]//2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025: 59-64.

[4] Yang X, Chen A, PourNejatian N, et al. A large language model for electronic health records[J]. NPJ digital medicine, 2022, 5(1): 194.

[5] Sivan R, Zukarnain Z A. Security and privacy in cloud-based e-health system[J]. Symmetry, 2021, 13(5): 742.

[6] Wu X, Bao W. Research on the Design of a Blockchain Logistics Information Platform Based on Reputation Proof Consensus Algorithm[J]. Procedia Computer Science, 2025, 262: 973-981.

[7] Zhang M. Discussion on Using RNN Model to Optimize the Accuracy and Efficiency of Medical Image Recognition[J]. European Journal of AI, Computing & Informatics, 2025, 1(2): 66-72.

[8] Ressi D, Romanello R, Piazza C, et al. AI-enhanced blockchain technology: A review of advancements and opportunities[J]. Journal of Network and Computer Applications, 2024, 225: 103858.

[9] Hui X. Medical Entity Recognition Based on Bidirectional LSTM-CRF and Natural Language Processing Technology and Its Application in Intelligent Consultation[J]. 2025, 6(1),1-8

[10] Wu H. From Large Language Models to Innovative Applications of Blockchain AI in Web3[J]. Journal of Computer, Signal, and System Research, 2025, 2(4): 11-17.