Healthcare data engineering
Across HL7 v2, FHIR, CDA, X12, DICOM, and proprietary EHR exports.
Unified, governed, and ready for intelligence.
The single largest barrier to healthcare AI is rarely the model - it is the data. Patient information sits across electronic records, lab systems, imaging archives, billing platforms, wearable feeds, and care management tools, each with its own format, vocabulary, and access controls.
Entiovi works with provider networks, payers, life sciences companies, and digital health platforms to build the data layer underneath everything else - interoperable, governed, and structured for intelligent use.
Entiovi designs and engineers healthcare data platforms that unify longitudinal patient information across source systems, apply consistent vocabularies and identifiers, enforce privacy and access policy at the data layer, and present analytics and AI workloads with the clean, traceable inputs they require. The work covers the full path from ingestion to governed consumption.
Data engineering in healthcare is not the same as data engineering elsewhere. Standards are dense and partial. Vocabularies overlap and conflict. Consent is a first-class concern. Entiovi's data platforms are built with these realities embedded in the architecture - not papered over in a downstream application layer.
Across HL7 v2, FHIR, CDA, X12, DICOM, and proprietary EHR exports.
Across networks, acquisitions, and partner systems.
Including OMOP, USCDI, PCORnet, and custom enterprise models.
With de-identification, tokenisation, consent management, and differential privacy.
Aligned with HIPAA, GDPR, HITECH, DISHA, and institutional policy.
With audit-grade lineage and access logging.
Across hospital, ambulatory, and home care settings.
And value-based care platforms.
For life sciences.
And risk-adjusted analytics.
With privacy-preserving access.
Across networks after mergers, partnerships, or expansion.
A single trusted patient view, faster analytics cycles, regulatory-grade data lineage, sharply reduced duplication and reconciliation effort, and a foundation that makes downstream AI viable rather than aspirational. Decisions made on top of this layer can be audited, reproduced, and defended.
Engagement begins with data discovery and a source-system audit. From there, Entiovi designs the target data model, builds the ingestion and quality engineering, implements governance and privacy controls, and hands over with a documented stewardship model - data ownership, change management, and metric instrumentation defined and owned before the platform goes into production use.
The strength of any clinical or scientific AI system is the data behind it. Entiovi treats that data layer with the same engineering seriousness as the models that depend on it.
Talk to Entiovi about putting this discipline into production - from design and engineering through ongoing operation, with delivery from India and regional presence in your market.