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Data Provenance

Data provenance records where data came from, how it changed, and whether it can be trusted for a workflow.

technicalPublished 2026/06/11Last verified 2026/07/17

Healthcare compliance context

This definition is for healthcare technology research only and is not security, legal, clinical, or compliance advice.

Data provenance describes the origin, history, transformation, and trust context of data. In healthcare AI, provenance can help users understand whether information came from the EHR, a patient form, a payer response, a device, a document, a model output, or a manual edit.

Buyers should verify how provenance is preserved through integrations, normalization, generated outputs, exports, and audit logs.

Application scenario: In workflow review, this term helps teams map a vendor claim to the care setting, data flow, integration point, user handoff, and oversight step where it applies. Procurement impact: Buyers should evaluate evidence, interoperability effort, security and privacy controls, pricing assumptions, support, and compliance responsibilities before shortlisting or contracting for a tool that depends on this capability.

Sources and review notes

These links support definition-level research and do not establish the regulatory status, safety, or suitability of any product.

W3C PROV-O models provenance through entities, activities, agents, derivation, generation, attribution, and time so provenance can be represented and exchanged across systems. HL7 FHIR's Provenance resource adapts that model to healthcare resources by recording the activity, target, source entities, and agents involved in creating, revising, deleting, signing, importing, or transforming data. FDA's drug CGMP data-integrity guidance describes metadata and audit trails needed to reconstruct creation, modification, deletion, and reprocessing in that regulated manufacturing context. These sources provide useful models and controls but do not make provenance equivalent to an audit log, prove that recorded assertions are true, or establish that data is complete, clinically correct, authorized, or fit for a downstream purpose; FHIR R5 Provenance is Trial Use and the FDA guidance is CGMP-specific. Buyers must test source identity, versioning, transformations, terminology mappings, timestamps, agents, model and prompt versions, manual edits, signatures, retention, export behavior, tamper resistance, and links between source evidence and generated outputs.

FAQs

What data provenance questions should buyers ask?
Ask where data came from, who changed it, when it was refreshed, how it was normalized, and whether generated outputs preserve source context.

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