A cardiovascular center hired a team of developers to develop an EHR for their use case, choosing FHIR as the standard. Within three months, the system was up and running.
A few months later, the organization expanded into sports medicine and needed to capture additional data related to blood pressure trends in athletes. The developers were called again, but this time, making changes wasn’t as straightforward. While FHIR allowed extensions, adapting the system to support new clinical concepts without breaking existing workflows proved challenging. To improve flexibility, they transitioned to openEHR, allowing them to define clinical models dynamically. As a result, every time new data needed to be added, it required significantly less development and deployment effort.
But why was FHIR difficult to modify? Let’s explore why openEHR offers greater flexibility and interoperability compared to FHIR. To understand the fundamentals of openEHR and what it is, read this earlier blog that defines some terms.
Why openEHR?#
When additional data needs be integrated into the existing system, openEHR plays a crucial role. It was designed to solve a fundamental problem in healthcare IT: the difficulty of maintaining, evolving, and sharing structured clinical data over time.
Traditional EHR systems and standards often struggle with flexibility, leading to high development costs and data silos. openEHR addresses these challenges through a separation of concerns, allowing healthcare organizations to adapt to new requirements without breaking existing systems. Finally a question may arise, didn’t the other health IT standards solve this, if not why do those exist? To understand that, let’s look at the next section.
Health IT standards: A detailed comparison#
Yes, openEHR is not the only standard in healthcare IT. Various other standards exist, each serving different purposes, from data exchange to clinical modeling and terminology management. Here are some of the most widely used standards in healthcare IT and their comparison with openEHR:
| Standard | Purpose | Key Features | How It Differs from openEHR | Relation to openEHR |
|---|---|---|---|---|
| FHIR (Fast Healthcare Interoperability Resources) | Data exchange & interoperability | API-driven (RESTful, GraphQL), Modular resources (Patient, Observation, etc.) - Extensible but less flexible for evolving data models | Focuses on exchanging data, not long-term storage, Less flexible for evolving clinical models | Works well with openEHR for interoperability - FHIR can retrieve structured openEHR data |
| HL7 v2 & v3 | Messaging & data exchange | HL7 v2: Widely used for hospital messaging (lab results, ADT), HL7 v3: XML-based but less adopted | HL7 v2 is message-based, not model-based, HL7 v3 was not widely adopted due to complexity | HL7 v2 is used in legacy systems, openEHR stores structured data, which can be exchanged via HL7 |
| CDA (Clinical Document Architecture | Document-based data exchange | XML-based clinical documents, Used for reports (discharge summaries, imaging reports) | Focuses on document sharing, not structured data modeling | Can complement openEHR for structured clinical documents |
| DICOM (Digital Imaging and Communications in Medicine) | Medical imaging standard | Standard for storing & transmitting medical images. Used in radiology, cardiology, and pathology | Focuses on medical images, not structured clinical data | openEHR can reference DICOM images but does not store them |
| SNOMED CT | Clinical terminology standard | Comprehensive system for diagnoses, procedures, and symptoms. Enables semantic interoperability | SNOMED CT is a terminology system, not a data storage model | Used within openEHR archetypes for consistent clinical coding |
| LOINC (Logical Observation Identifiers Names and Codes) | Standardized lab tests & clinical measurements | - Standardized codes for lab results (e.g., blood pressure, glucose tests). Widely used in diagnostics | LOINC standardizes lab test names but does not store patient data | Works alongside openEHR to standardize lab data in archetypes |
How openEHR Outperforms FHIR#
FHIR was once the go-to standard for healthcare interoperability and data exchange due to its simplicity and widespread adoption. However, as healthcare systems evolve and require more structured, adaptable, and long-term clinical data management, FHIR’s limitations have become apparent. Many organizations are now shifting toward openEHR, which provides a more flexible and sustainable approach to healthcare data modeling.
FHIR is still widely used for data exchange, but when it comes to long-term clinical data management, adaptability, and structured data modeling, openEHR proves to be more powerful. Here’s why:
Flexibility in Data Modeling#
| Aspect | openEHR | FHIR |
|---|---|---|
| Clinical Data Modeling | Uses archetypes & templates, allowing highly structured, reusable clinical models. | Uses fixed “resources”, which can be extended but lack deep clinical modeling. |
| Custom Data Fields | Can add new data fields without changing the system architecture. | Adding new fields often requires creating new resource definitions or extensions. |
| Example | A hospital can easily add a new vital sign (e.g., respiratory effort) without disrupting the system. | Requires modifying the existing FHIR Observation resource or creating a custom extension. |
Why it matters: openEHR allows healthcare organizations to adapt quickly to new requirements, unlike FHIR, which often requires modifications to resource definitions.
Long-Term Data Persistence & Evolution#
| Aspect | openEHR | FHIR |
|---|---|---|
| Data Storage Model | Designed for long-term, structured clinical data storage. | Designed for data exchange, not long-term structured storage. |
| Data Versioning | Supports full version control of clinical data, ensuring traceability. | Does not inherently support versioning for historical data changes. |
| Example | A patient’s longitudinal health record remains structured and queryable over decades | Data is typically transient, and changes require overwriting or maintaining separate copies. |
Why it matters: openEHR ensures data remains structured, versioned, and queryable, whereas FHIR is primarily meant for real-time exchange, not permanent structured storage.
Interoperability & Standardization#
| Header | Header | Header |
|---|---|---|
| Interoperability with Other Standards | Works well with SNOMED CT, LOINC, DICOM, and FHIR. | Designed for interoperability, but lacks deep clinical data modeling. |
| Standardized Terminologies | Can integrate multiple healthcare terminologies directly into archetypes. | Uses codeable concepts, but data structuring is less flexible. |
| Example | An openEHR blood pressure archetype can embed LOINC codes for lab data and SNOMED CT terms for diagnoses. | FHIR would require separate extensions or additional resources. |
Why it matters: openEHR provides built-in interoperability and allows healthcare providers to reuse structured clinical models across different systems.
Reduced Development & Maintenance Effort#
| Aspect | openEHR | Header |
|---|---|---|
| Developer Effort | Once archetypes are built, they can be reused across different applications. | Developers must constantly extend or modify FHIR resources to fit new use cases |
| Implementation Time | Faster due to predefined, reusable clinical models. | Longer since FHIR requires creating new extensions for evolving data needs. |
| Example | A hospital implementing new cardiovascular risk scores can reuse existing archetypes. | In FHIR, a custom extension must be developed and validated. |
Why it matters: openEHR reduces long-term maintenance costs and allows developers to focus on innovation rather than reworking data structures.
Using openEHR in Practice: Servers, Open-Source Tools, and Implementation#
Now that we understand the core concepts of openEHR, let’s explore how it is used in real-world implementations. This section will cover:
- openEHR Servers – The backbone of openEHR-based applications.
- Open-Source Tools and SDKs – Libraries and frameworks to build applications.
- Modeling and Development Tools – Tools for creating archetypes, templates, and queries.
- Deployment Strategies – How organizations implement openEHR in practice.
openEHR Servers: The Backbone of Implementation#
An openEHR server is responsible for storing, managing, and retrieving clinical data based on openEHR models. These servers expose REST APIs to interact with different applications (EHR systems, patient apps, and decision-support systems).
| Server | Description | License | Programming Language | Article |
|---|---|---|---|---|
| EHRbase | A scalable, production-ready openEHR server backed by a strong community. | Open-source (Apache 2.0) | Java | Setup |
| Better Platform | A commercial-grade openEHR implementation with cloud support. | Proprietary (free for development) | Java | Cell |
Key Features of openEHR Servers
- Support for Archetypes & Templates – Data is stored according to structured models.
- Query Support (AQL - Archetype Query Language) – Enables powerful clinical data retrieval.
- Versioning & Auditability – Ensures full traceability of all changes.
- Integration via REST APIs – It easily connects to other healthcare IT systems.
Open-Source Tooling & SDKs for Development#
If you’re building applications using openEHR, various SDKs and libraries can help with integration, data modeling, and querying.
| SDK / Library | Description | Language |
|---|---|---|
| EHRbase SDK | Java SDK for interacting with the EHRbase server. | Java |
| openEHR-SDK | A powerful SDK for working with openEHR data models. | Java |
Key Features of These SDKs
- Simplifies Data Exchange – Helps in reading/writing structured openEHR data.
- Automates Query Execution – Allows developers to use AQL without manual effort.
- Standardized API Access – Makes it easier to interact with different openEHR servers.
Modeling and Development Tools#
To create archetypes, templates, and queries, openEHR provides various graphical and CLI-based tools.
| Tool | Purpose | License |
|---|---|---|
| Archetype Designer | Web-based tool for creating archetypes and templates. | Free |
| Template Designer | Allows building templates from multiple archetypes. | Free |
| openEHR CKM (Clinical Knowledge Manager) | Repository of standardized archetypes and templates. | Open-source |
| AQL Console | Query testing tool for openEHR databases. | Open-source |
Key Benefits of These Tools
- No Code Required – Clinicians and developers can design models using visual tools.
- Standardized Knowledge Sharing – Archetypes/templates can be shared globally.
- Faster Development – Pre-built models speed up application development.
Deployment Strategies: How openEHR is Used in Practice#
On-Premise Deployment Many large hospitals and national health systems deploy openEHR servers on their own infrastructure to ensure data security and compliance. Example: NHS (United Kingdom) uses openEHR for longitudinal patient records.
Cloud-Based Deployment Organizations with scalability needs deploy openEHR on cloud platforms. Example: Better Platform offers an openEHR-based cloud solution for hospitals.
Hybrid Models Some organizations use a mix of on-premise storage for sensitive data and cloud services for analytics & AI. Example: Research institutions store clinical data securely while running analytics on cloud platforms.
Our Take: Why This Matters#
Many developers and healthcare professionals are still unfamiliar with openEHR, even though it provides a future-proof, scalable way to handle clinical data. The challenge isn’t just adoption, it’s about shifting the mindset from traditional, rigid EHR systems to something flexible and patient-centered. If you’re a developer, this is your chance to work on cutting-edge healthcare applications without being locked into vendor-specific solutions. If you’re a healthcare professional, understanding openEHR will help you advocate for better systems in your organization.
Conclusion#
openEHR is transforming healthcare IT by providing a flexible, future-proof approach to clinical data management. Unlike traditional EHR systems and even FHIR, openEHR separates data from applications, allowing healthcare organizations to adapt, evolve, and integrate new requirements without costly redevelopment.
We explored how openEHR’s archetypes and templates enable structured, reusable clinical models, making it easier to maintain and share healthcare data across different systems. We also discussed open-source openEHR servers, tools, and modeling concepts that make implementation more accessible than ever.
While FHIR remains essential for data exchange, openEHR is increasingly recognized as the better choice for long-term data storage and management. As more organizations adopt openEHR, we’re witnessing a shift toward truly interoperable, patient-centric healthcare systems.
Further Reading#
We encourage you to explore openEHR further, check out the links we’ve included throughout the article, and join our openEHR webinar, which covers key topics like openEHR architecture, modeling, AQL, and open-source tools.
To dive deeper, visit openEHR.org for official documentation and community updates. You can also explore open-source projects like EHRbase to get hands-on experience.