Machine-Enforceable Data Contracts for Trusted Healthcare Data Products
Keywords:
Healthcare data contracts, FHIR, USCDI, interoperability, data quality, data products, healthcare analytics, provenance, patient identity, healthcare AI, data governanceAbstract
Healthcare interoperability is improving quickly, but successful exchange is not the same as trustworthy use. A FHIR resource can be syntactically valid and still be incomplete, semantically ambiguous, associated with the wrong patient, stale for the intended workflow, missing provenance, or inappropriate for a given purpose of use. This gap becomes more consequential as U.S. healthcare moves toward broader FHIR-based exchange, electronic prior authorization, CMS-aligned networks, bulk data movement, patient-facing applications and AI-assisted operations. This article proposes a machine-enforceable Healthcare Data Contract Assurance Framework (HDCAF) that places a runtime trust boundary between data exchange and data consumption. The framework evaluates seven dimensions: structural conformance, clinical semantics, identity integrity, quality and temporal fitness, provenance, privacy and permissible use, and operational service levels. It further introduces a Purpose-Specific Healthcare Data Trust Score (P-HDTS), which recognizes that trust is contextual: data acceptable for exploratory analytics may be unacceptable for a patient-facing or automated operational workflow. The framework combines weighted trust scoring with non-negotiable hard gates so that critical identity, privacy or semantic failures cannot be averaged away. A multi-source Patient 360 example demonstrates how contracts can be versioned, enforced, observed and used to certify healthcare data products before they feed analytics or AI. The paper’s central argument is simple: standards such as FHIR and USCDI enable exchange; executable data contracts provide the operational assurance needed to decide whether exchanged data is fit for a specific healthcare purpose.
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