Zero-Trust Data Architecture for Multi-Hospital Research: HIPAA-Compliant Unification of EHRs, Wearable Streams, and Clinical Trial Analytics
Increasingly sophisticated clinical studies spanning multiple hospitals now require architectures that securely integrate Electronic Health Records, wearable device streams, and trial-related analytics. Legacy perimeter-based security, permitted by earlier data-sharing agreements, no longer meets the stringent privacy requirements imposed by HIPAA and similar regulations. In response, this article outlines a Zero-Trust Data Architecture designed explicitly for healthcare research. Its core policies—continuous verification, least-privilege provisioning, and micro-segmented networks—guard clinical data by ensuring that every requester can prove their identity before being permitted access to the narrowest, most relevant dataset. The presented architecture conforms to open standards, directly maps to NIST Special Publication 800-207, and incorporates tools such as the Open Policy Agent, cryptographically secured application programming interfaces, and cloud-native activity monitors. Usability and effectiveness are demonstrated in a simulation of three collaborating oncology centers that pooled information from multiple EHR vendors, streaming wearables, and an external trial management platform. Results show marked gains in early adverse-event flagging, patient follow-up, and cross-institution analytics, all accomplished within an audit trail that meets HIPAA safeguards. Additional sections address data-sample harmonization via the Fast Healthcare Interoperability Resources specification, ontology bridging to preserve clinical meaning, and pipeline encryption from source to storage. Residual obstacles—proprietary interfaces, variance in wearable metadata, and organizational inertia—are acknowledged but do not diminish the conclusion that the proposed ZTDA model advances secure, cooperative, and privacy-respecting research practice for contemporary health networks.
Authors
- K. Chadha
Publication Details
- Journal
- International Journal of Computational and Experimental Science and Engineering
- Published
- 2025-07-14
- DOI
- https://doi.org/10.22399/ijcesen.3477
- Citations
- 1
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.48