openEHR Implementation
openEHR is a vendor-neutral, open standard for clinical data. It separates the clinical knowledge model from the software — defined through archetypes and templates — from the technical infrastructure that stores and retrieves data. Implementing openEHR correctly requires expertise across both layers: the clinical modeling that determines what data gets stored and how it is structured, and the integration work that connects the CDR to the systems around it. We cover both.
Where openEHR implementations go wrong
openEHR adoption is growing, but many implementations stall or produce clinical data that cannot be queried, exchanged, or trusted — because the modeling and integration layers were not built on solid foundations.
Archetypes designed without clinical validation
Archetypes that are created quickly to match an existing database or application form — rather than modeled around the clinical concept — produce data that cannot be reused across use cases, cannot be queried with AQL in any meaningful way, and cannot be exchanged with other openEHR systems using the same archetype.
CDR isolated from the rest of the system
An openEHR CDR that is not properly connected to front-end applications, analytics tools, or partner systems delivers limited value. Without clean REST API integration, FHIR interoperability, or migration paths from legacy systems, the CDR becomes a data silo despite being technically standards-compliant.
No conformance verification
Many openEHR implementations are never formally validated against the specification. The CDR may store and retrieve data, but the composition structure, archetype bindings, template constraints, and API behavior may deviate from the standard in ways that surface only when interoperating with another compliant system or auditing for regulatory purposes.
How we help
We work with healthcare teams and software vendors at every stage of an openEHR project — from initial modeling to production integration and conformance verification.
Requirements Analysis
We establish what clinical data needs to be captured, which use cases drive the data model, what query and reporting requirements exist, and what interoperability targets the system must meet. This scoping determines which archetypes to use or design, what the template structure should be, and how the CDR needs to be connected to the surrounding systems.
Clinical Data Modeling
We design openEHR archetypes and templates for the clinical concepts your system needs to capture. We start from existing CKM archetypes where possible and design new archetypes only when the clinical concept is not adequately covered. Templates are designed for each use case and constrained to enforce data quality at the point of entry. All models are documented and versioned.
CDR Design
We design the Clinical Data Repository architecture: CDR selection or build decision, deployment model, access control structure, versioning and audit strategy, and the service layer that exposes clinical data to consuming applications. We design the CDR to meet both current requirements and the integration and query demands that will arise as the system grows.
REST API Integration
We design and implement integrations using the openEHR REST API — connecting front-end clinical applications, analytics dashboards, and mobile clients to the CDR. We also design openEHR-to-FHIR integration layers for systems that need to expose openEHR data as FHIR resources or receive data from FHIR-based sources.
Legacy Data Migration
We design and implement migration pipelines that transform data from legacy clinical systems — relational databases, HL7 v2 message archives, CSV exports — into openEHR compositions. Migration includes source analysis, mapping design, transformation logic, data quality checks, and validation that the resulting compositions conform to the target archetypes and templates.
Testing and Conformance Verification
We test openEHR implementations at multiple levels: functional testing of composition creation, retrieval, and AQL queries; load testing to validate CDR behavior under realistic data volumes; and conformance verification that checks the implementation against the openEHR specification. For existing implementations, we audit the current state and deliver a prioritized report of deviations and recommendations.
Do you have any questions?
Let us know how we can help you.