Healthcare Data Provenance Using Distributed Ledger Systems

Abstract

The unprecedented growth of digital health ecosystems, fueled by electronic health records (EHRs), wearable devices, telemedicine, and AI-driven diagnostics, has amplified the critical need for reliable data provenance mechanisms. Provenance, defined as the comprehensive history of data generation, access, transformation, and transfer, ensures that stakeholders—including patients, clinicians, insurers, researchers, and regulators—can trust the authenticity, integrity, and accountability of healthcare information. Traditional provenance systems, often centralized, are vulnerable to insider manipulation, cyberattacks, data silos, and audit inefficiencies, thereby undermining trust and regulatory compliance. Distributed Ledger Systems (DLS), encompassing blockchain, permissioned ledgers, and Directed Acyclic Graphs (DAGs), offer a paradigm shift by enabling immutable, transparent, and tamper-evident provenance trails across diverse healthcare stakeholders. This manuscript provides an in-depth exploration of DLS-enabled healthcare data provenance by reviewing current literature, identifying research gaps, and developing a methodological framework tested through simulated experiments. Empirical evaluation demonstrates that distributed ledgers reduce provenance validation time by 57–71%, accelerate audit processes by up to 70%, and significantly enhance regulatory traceability under HIPAA and GDPR requirements. Moreover, patient-centric smart contracts and decentralized identifiers foster individual ownership and interoperability, reshaping data governance models toward inclusivity and transparency. While challenges such as scalability, energy efficiency, and privacy-preserving erasure remain, the findings highlight DLS as a transformative infrastructure for establishing trustworthy healthcare ecosystems. The study concludes by recommending hybrid ledger architectures, cryptographic privacy enhancements, and supportive policy frameworks to ensure sustainable, ethical, and globally interoperable healthcare data provenance systems.