Blockchain Technology to Enhance Clinical Data Management in Healthcare: A Systematic Literature Review.

Abstract

This systematic review examines how blockchain is applied in clinical data management (CDM) and what prevents its adoption in healthcare. A structured search in Scopus and Web of Science retrieved 554 records; after applying inclusion/exclusion criteria and quality assessment, 32 studies published between 2018 and 2024 were included. The analysis was guided by five research questions: (1) how blockchain supports clinical data workflows; (2) its role in data security and privacy; (3) key technical challenges and commonly used technologies; (4) integration with other healthcare technologies, and (5) how does blockchain technology integrate with and enhance other emerging healthcare technologies? Findings show that blockchain can support consent management, secure data sharing, traceability, and tamper-resistant audit trails using smart contracts and decentralized access control. It is also positioned as a trust layer for electronic health records, the Internet of Medical Things, artificial intelligence, and telemedicine by ensuring integrity and controlled access to sensitive clinical data. However, several barriers limit real-world deployment. Reported challenges include limited scalability and throughput, difficulty integrating with legacy electronic health record systems, heterogeneous regulatory requirements, and the complexity of encoding privacy, consent, and compliance into smart contracts. Ethereum and Hyperledger Fabric are the most frequently implemented platforms, often combined with off-chain storage and interoperability standards such as Fast Healthcare Interoperability Resources (FHIR)/Substitutable Medical Applications and Reusable Technologies on FHIR. Overall, blockchain shows strong potential to improve security, transparency, and cross-institution exchange in CDM, but its viability depends on addressing scalability, interoperability, and governance constraints. However, the evidence base remains heterogeneous, and only a minority of studies report quantitative benchmarks or real-world deployments, which limits cross-study comparability and generalizability.