A Unified HIPAA-Compliant De-Identification Architecture: Six Production-Proven Frameworks Across Structured, Unstructured, Mainframe, Big Data, EDI, and Hybrid Healthcare Environment
DOI:
https://doi.org/10.15680/IJCTECE.2025.0803011Keywords:
HIPAA compliance, data de-identification, healthcare data, structured data, unstructured data, big data, hybrid systems, privacy protection, medical imagingAbstract
This paper proposes a homogeneous, HIPAA-compliant de-identification architecture built on six production-hardened frameworks. These frameworks serve non-production IT environments, such as development, testing, staging, and analytics. All six frameworks are architected from an enterprise IT perspective inside a major health insurance company. The problem they address is allowing software development and test teams to test against production-sized, non-sensitive data without revealing protected health information (PHI), personally identifiable information (PII), or payment card information (PCI). Six disparate environments and data models were used, including: unstructured data stores; structured relational databases; big-data distributed processing frameworks (Hadoop/Hive/Spark); mainframes (IMS/DB2); EDI (834/835/837) flat files; and relational-application hybrids. Together, these six frameworks provide a single, reusable reference architecture for consistent HIPAA-compliant de-identification provisioning across heterogeneous enterprise environments. Case studies and analysis metrics demonstrate the benefits of a hybrid approach to de-identification, including greatly increased data security and improved consistency and usability of the provisioned datasets. Though each framework individually offers the optimal de-identification strategy for its domain, a composite of them combined yields the best overall solution, and the future offers the integration of machine learning and blockchain technologies for further accuracy and compliance.
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