Transforming Clinical Data Security and Management through Blockchain
Abstract
This paper presents a narrative review of the scientific literature on the application of blockchain technology in healthcare data security and management. A structured search of PubMed, Scopus, and Google Scholar covering January 2017 to January 2026, using a consistent search string and eligibility criteria, identified 30 relevant studies addressing blockchain in clinical and health data contexts; each study was appraised with a quality instrument adapted from MMAT and SANRA and classified by level of evidence. The reviewed evidence indicates that blockchain's decentralized, cryptographically secure, and immutable architecture offers theoretical advantages for health data interoperability, patient privacy, electronic health record integrity, clinical trial transparency, and pharmaceutical supply chain authentication. Real-world pilot implementations, including Estonia's national health data infrastructure and the FHIR Chain project, provide preliminary feasibility evidence, and the comparative analysis is anchored in published quantitative benchmarks for throughput, latency, and energy as well as an explicitly modeled national hospital workload. However, the current evidence base remains predominantly conceptual; large-scale empirical validation comparing blockchain with existing systems in terms of clinical outcomes, system performance, and cost-effectiveness is largely absent. Key barriers to adoption include regulatory conflicts with GDPR and HIPAA, scalability limitations, high implementation costs, and a lack of interoperability standards. These limitations must be acknowledged before broad recommendations for adoption can be made. Collaborative efforts among healthcare providers, technologists, regulators, and policymakers, supported by rigorous empirical research, will be essential to determine whether blockchain offers demonstrable advantages over current technologies and to guide its responsible integration into health systems.