Blockchain Smart Contracts for Automating Clinical Trials: Systematic Review and Proposed System Architecture

Clinical Snapshot

Key Findings

• P Value: Not applicable for systematic review

• Effect Size: Rapid patient matching (6000 simulated patients in 2.13 seconds)

• Primary Outcome: Smart contract applications across clinical trial lifecycle with 90% implementing protocol enforcement and audit logs

• Nnt Or Sensitivity: 70% of implementations used Ethereum-based networks, 60% included consent management features

• Confidence Interval: Not reported

Clinical Application

Low feasibility for immediate clinical implementation due to regulatory uncertainty, scalability issues, and lack of real-world validation Significant regulatory challenges with TGA approval processes, Privacy Act compliance, and integration with existing Australian clinical trial infrastructure would need resolution before implementation Currently limited to research and development settings due to prototype-only evidence

Abstract

BACKGROUND: Blockchain technologies have revolutionized the financial sector through their ability to generate immutable, cryptographically secure records. Clinical trials and health care data possess several synergies with those of the financial sector, specifically pertaining to the importance of tamper-resistant recording of processes. The evolution of blockchain to autonomously execute tasks contingent upon predefined contractual terms via smart contracts (SCs) allows a dynamic chain of interlinked events to unfold independently and in sequence, with time-stamped records. In recent years, mistrust in clinical trial data has grown significantly. Recording the entire clinical trial lifecycle from application, registration, recruitment to conduct, finance management, statistical analysis, and reporting in an immutable, cryptographically secure ledger with SC execution of trial processes could limit the potential for human intervention and tampering. This would produce a time-stamped record of all events within the trial lifecycle. Leveraging the capabilities of SCs could alleviate recruitment challenges and address ongoing concerns regarding data transparency, ownership, and integrity that currently undermine clinical trial processes. OBJECTIVE: This study aimed to review the existing literature on SC applications in clinical trials and propose a system architecture for using SCs to automate key processes throughout the clinical trial lifecycle. METHODS: A systematic search was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, identifying peer-reviewed studies and open-source repositories pertaining to the implementation of SCs in clinical trials. Data were extracted specific to the stage of the trial lifecycle described, SC architecture, and technical specifications for real-world implementation. Data were synthesized to propose an architecture for automating clinical trial processes within the lifecycle using SCs. RESULTS: A total of 144 records were screened; 10 studies met the inclusion criteria. Most implementations used private Ethereum-based networks (7/10, 70%). Reported applications included automated patient matching (5/10, 50%), consent management with dynamic permissioning (6/10, 60%), protocol enforcement and time-stamped audit logs (9/10, 90%), adverse event reporting (3/10, 30%), and financial or workflow automation (3/10, 30%). SC-based recruitment systems demonstrated rapid matching performance (eg, 6000 simulated patients matched in 2.13 s). However, all included systems were prototypes or simulations, and none were tested in real-world regulatory settings. Scalability, interoperability limitations, regulatory ambiguity (eg, General Data Protection Regulation right-to-erasure conflicts), and high infrastructural complexity were common gaps noted across studies. CONCLUSIONS: Current evidence suggests that SCs can enhance transparency, traceability, and automation throughout the clinical trial lifecycle. However, the literature remains dominated by simulation-based prototypes, primarily Ethereum-dependent architectures, and lacks analyses of cost-effectiveness, governance, and integration with institutional workflows. Future research should evaluate hybrid architectures, develop interoperability standards, and assess regulatory and ethical implications in real deployments.

References

1. 1.Sheikh, Z., Samarth, G., & Jaffer, U. (2026). Blockchain smart contracts for automating clinical trials: Systematic review and proposed system architecture. JMIR Medical Informatics, 12(1), e76980. https://doi.org/10.2196/76980