The Problem with Proprietary Clinical Data Models
Health systems across the United States have invested heavily in electronic health record platforms, yet most remain locked into proprietary data models that were designed for billing workflows rather than clinical decision-making. The consequence is predictable: clinical data trapped in vendor-specific formats, difficult to query, harder to share, and nearly impossible to govern at the archetype level.
When a health system attempts to extract structured clinical observations from its EHR for population health analytics or quality measurement, the effort typically requires custom ETL pipelines, manual mapping exercises, and ongoing maintenance as the vendor updates its schema. This fragility is not a technology problem. It is an information architecture problem.
openEHR addresses this by separating the clinical knowledge model from the technical storage layer. Archetypes define the clinical meaning of data elements once, and that definition remains stable regardless of which system stores or transmits the data. This separation is what makes openEHR consequential rather than merely interesting.
What openEHR Archetypes Actually Provide
An openEHR archetype is a formal, computable definition of a clinical concept. OBSERVATION archetypes capture measured or observed clinical data such as vital signs, laboratory results, or symptom assessments. EVALUATION archetypes represent clinical judgments and interpretations. Each archetype is developed through international clinical review and published in the Clinical Knowledge Manager.
The practical benefit is that a blood pressure observation recorded in one system using the archetype carries the same semantic structure when consumed by another system. There is no mapping table. There is no transformation logic. The archetype is the contract between producer and consumer.
This stands in contrast to HL7 FHIR resources, which define a wire format for data exchange but leave significant room for profiling and extension. FHIR solves the interoperability transport problem. openEHR solves the clinical meaning problem. Both are necessary, and the strongest architectures use them deliberately together.
Why Adoption Has Been Slow in the United States
The predominance of a small number of EHR vendors in the US market has created an environment where proprietary data models are the default. Most health system CIOs did not choose their clinical data architecture. They inherited it with their EHR contract. Changing that architecture requires clinical informatics leadership, governance structures, and a willingness to invest in data infrastructure that does not produce immediate revenue.
Additionally, the US regulatory environment has historically emphasized HL7 messaging standards and more recently FHIR for interoperability mandates. openEHR has thrived in markets where national health data strategies required vendor-neutral clinical repositories, notably in parts of Europe, Australia, and Southeast Asia. The US has not yet mandated similar vendor-neutral persistence, though the direction of ONC policy suggests this is changing.
A Practical Path for Health Systems Ready to Begin
Health systems do not need to replace their EHR to adopt openEHR principles. The most pragmatic starting point is the clinical data repository layer: a vendor-neutral persistence tier that stores clinical data using openEHR archetypes alongside the EHR, not instead of it.
This approach allows the health system to begin governing clinical data definitions independently of the EHR vendor, build analytics and decision support on stable semantic foundations, and prepare for future interoperability mandates. The investment is in clinical informatics governance, archetype curation, and integration engineering, areas where Selah Digital has deep practitioner experience.
The question is not whether your health system will need vendor-neutral clinical data architecture. The question is whether you will design it deliberately or have it imposed by regulation.