Custom integration backlog
Maintaining manual code across legacy EHR and DICOM feeds stalls new integrations for months.


Maintaining manual code across legacy EHR and DICOM feeds stalls new integrations for months.
Clinical AI initiatives stall because patient context remains locked inside proprietary data silos.
Fragmented feeds lack centralized audit trails, risking perimeter leakage and regulatory non-compliance.
Ingest bidirectional HL7 v2.x, FHIR R4, DICOM, and X12 EDI directly at your perimeter without point-to-point scripting.
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Pipes such as HL7 inbound with FHIR output convert each message to canonical FHIR in a single pass, so every downstream consumer reads the same record.
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Run Tietai alongside your current interface engine, validate the parallel feeds and cut over only when the outputs match.
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Track every connected system and device in one inventory, trace messages through the mesh with OpenTelemetry and replay failures from the dead-letter queue.
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Deploy on-premise under ENS Alta to retain full control over patient records.
Unify disparate data sources into canonical FHIR without custom code.
Validate parallel feeds before cutover, so clinical services keep running.
Expose governed FHIR APIs and Bulk Data directly to research workloads.
Route remote patient feeds into EHRs through 200+ native connectors.
Tiet · Connect has no medical purpose, so it deploys as data infrastructure without medical-device certification.
EHRs such as Epic, Oracle Cerner and Dedalus, PACS, laboratory and pharmacy systems, connected devices, SAP Healthcare and national infrastructure across Europe, through 200+ native connectors for HL7 v2, FHIR R4, DICOM, X12 and openEHR.
Tiet · Connect keeps validating the integrations it depends on and watches for drift, such as a changed endpoint, a new schema or a broken contract. When it finds one, it holds the affected flow and raises the issue before anything acts on bad data.
Yes. Tiet · Connect runs on-premise or air-gapped. Across several sites, the mesh runs each step where it is allowed to run, and only de-identified, derived results cross a boundary.
Yes. You can follow each message through its route and transformation, and messages that fail wait in a dead-letter queue until your team reviews and replays them.
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