Automating Compliance in Clinical Data Pipelines Using Policy-as-Code, NLP, and Blockchain-Inspired Auditing

Rapidly growing clinical data, augmented by digitization of health records and multi-site studies, is of tremendous potential but also an emerging regulatory challenge. Maintaining pace with new healthcare regulations such as HIPAA, GDPR, and 21 CFR Part 11 remains a top inhibitor to scaling clinical data pipelines. Traditional manual compliance checks take time, are error-prone, and are not well-suited for high-throughput data pipelines designed for today. In this paper, we outline a layered, automated compliance framework using policy-as-code, real time auditing, metadata tagging, and user-facing dashboards as part of clinical data infrastructure. We discuss the state of the art, define best practices in architectural design and discuss the primary challenges in developing automation at scale. The paper aims to offer health professionals, researchers, and technologists hands-on information to transform compliance into a bottleneck into a strategic enabler for secure and compliant innovation.

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