openEHR
An open standard for electronic health data.
What is openEHR? A simple explanation.
It is an open standard that provides a flexible and interoperable basis for collecting, storing, retrieving, and exchanging health data, regardless of which system or application generates or uses this data. openEHR is more than just software; it is a detailed specification developed and maintained by an independent, non-profit organization. This specification is based on open, freely accessible models and languages that enable health data to be structured and interpreted in a standardized way.
A key difference from traditional electronic health records is that openEHR strictly separates data storage from the applications that use this data. In traditional systems, data is often closely tied to specific software, which makes exchange and long-term use difficult. openEHR, on the other hand, creates a neutral data foundation that various applications can access and work with.
This separation leads to a significant increase in flexibility and avoids dependence on individual software providers, a phenomenon often referred to as “vendor lock-in.” As a result, healthcare institutions can gradually modernize their IT landscape without having to migrate all their data when changing systems. This approach also promotes innovation, as new applications can more easily access existing, standardized data.
The main purpose of openEHR in healthcare is to improve interoperability—the ability of different information technology systems to exchange data and use the exchanged information. In an ideal healthcare system, all parties involved in a patient's treatment – doctors, nurses, laboratories, hospitals – should have seamless access to relevant information. openEHR aims to make this possible.
The goal is to create a lifelong, patient-centered electronic health record (EHR) in which all relevant health information about a person is stored securely and in a structured manner and can be accessed when needed.
The future-proof foundation for digital health data: openEHR
In the age of digitalization, the healthcare sector faces the immense task of effectively managing the ever-growing volume of patient data and making it usable for improved care. Traditional methods and proprietary systems are increasingly reaching their limits, especially when it comes to the seamless exchange of information between different actors and systems. In this context, openEHR, an open standard for electronic health records (EHRs), is becoming increasingly important. The following lines highlight the most important aspects of openEHR for a non-technical audience, explain its advantages, identify ideal and less suitable use cases, and present successful examples from Europe and around the world.
How does openEHR provide support?
Simple analogies can be helpful in illustrating the more complex technical concepts of openEHR:
Lego bricks
A template could, for example, represent a complete discharge letter composed of various archetypes. Ultimately, these templates form the basis for a patient's actual electronic health record (EHR).
Library
The archetypes are then like the books in the library, containing specific knowledge on various medical topics. The collection of these “books” can change and grow over time without affecting the basic structure of the “shelves.”
Code languages
The reference model could also be compared to the syntax of a programming language. It defines the basic rules and structures for representing health data. The archetypes and templates would then be like specific programs or applications written in this “language” to perform certain tasks in healthcare.
These comparisons are intended to illustrate that openEHR is based on a modular and standardized architecture that focuses on flexibility and interoperability.
What are the advantages?
openEHR offers a number of key advantages over traditional electronic health record approaches and other standards such as FHIR. These advantages help to improve the efficiency, quality, and sustainability of healthcare.
One of the most important advantages is the ability to store data long term and future-proof it. openEHR was designed from the ground up to keep health info safe and accessible for really long periods of time, ideally for a whole lifetime (“data for life”). The aforementioned separation of data and applications plays a crucial role here. Since the data is stored in a standardized, vendor-neutral format, it is not dependent on the lifespan of a particular software. Even in the event of technological changes or a change in the IT system, valuable patient data is retained and can continue to be used. Another important aspect is data versioning, which is an integral part of the openEHR architecture. Every change to a patient's electronic health record is logged and stored so that the history of the data can be traced at any time. This is not only important for patient care, but also for audits and compliance with regulatory requirements.
Another key advantage of openEHR is semantic interoperability. This means that different IT systems in healthcare not only understand the exchanged information technically, but can also interpret its meaning correctly. openEHR achieves this by using standardized clinical models, known as archetypes, and linking to internationally recognized terminologies such as SNOMED CT and LOINC. Archetypes define the structure and content of clinical data points in a way that is unambiguous and interpretable by different systems. The connection to standardized terminologies ensures that medical terms and codes have the same meaning across systems. This semantic interoperability is crucial for the exchange of health data across different systems and organizations without losing meaning or misinterpreting it. This can reduce medical errors, improve the quality of care, and increase efficiency in healthcare.
Imagine, for example, that a patient is referred by their family doctor to a specialist. Thanks to semantic interoperability, both doctors can access the same, precisely defined information and be sure that they understand the patient's medical history and findings correctly.
In addition, openEHR is characterized by its high flexibility and adaptability. The two-level modeling that forms the foundation of openEHR separates the stable reference model from the archetypes that represent clinical knowledge. This separation makes it possible to map new clinical concepts and changing medical knowledge by creating or modifying archetypes without having to change the underlying technical infrastructure. In addition, templates offer the possibility of adapting archetypes for specific use cases or local needs. For example, a template can be created for the documentation of a specific clinical picture that contains only the relevant data points from various archetypes and presents them in a user-friendly form. This flexibility allows healthcare institutions to optimally adapt their systems to their individual workflows and changing requirements without compromising basic interoperability.
Compared to traditional EHR systems, openEHR offers standardized and future-proof data storage that overcomes the problems of proprietary and often incompatible systems. It promotes interoperability from the outset (“by design”). Compared to the FHIR (Fast Healthcare Interoperability Resources) standard, which is primarily focused on data exchange between systems, openEHR's main focus is on the long-term, detailed, and semantically rich modeling and persistence of health data. While FHIR is well suited for rapid integration and the development of simple applications, openEHR provides a more robust foundation for complex clinical data and long-term archiving. However, it is important to emphasize that both standards can be viewed not as competitors, but rather as complementary technologies that can play to their respective strengths in different scenarios or even be combined. The clear separation of data modeling and data exchange in openEHR can lead to more robust and long-term maintainable systems, while FHIR is better suited for scenarios where the focus is on fast and uncomplicated data exchange.
What are the strengths?
CHANCES.
Research projects: Standardized data storage and the ability to perform precise semantic queries make openEHR an ideal foundation for research projects based on high-quality, homogeneous data.
The FAIR principles (Findable, Accessible, Interoperable, Reusable), which are of great importance for scientific data management, are well supported by the design principles of openEHR.
Long-term patient care: The focus on lifelong patient records and the ability to store and manage data over very long periods of time make openEHR ideal for long-term patient care, especially in the management of chronic diseases.
CHALLENGES.
Simple data exchange scenarios: For scenarios that primarily involve the exchange of data between systems and do not require detailed semantic interoperability, the FHIR standard could be a leaner and potentially easier-to-implement solution.
Smaller projects with limited resources: The initial complexity of the openEHR specifications and the associated learning curve can be challenging for smaller projects or teams with limited resources. Modeling with archetypes and templates requires a deep understanding of clinical processes and can be time-consuming.
It is important to emphasize that the choice of the appropriate standard depends heavily on the specific requirements and objectives of a project. In many cases, a combination of openEHR and FHIR may be the optimal solution, with openEHR being used for persistent, semantically rich data storage and FHIR for flexible data exchange.
The use of data
openEHR is proving to be a valuable foundation for medical research, both for primary and secondary use of health data. The underlying principles of openEHR aim to make data coherent, structured, and patient-centered, and to store it independently of applications. This results in a high-quality, homogeneous database that allows researchers to focus on their actual questions instead of having to spend time and resources on data integration.
Primary use
Secundary use
openEHR supports data collection in research projects through standardized clinical models (archetypes) and templates. These ensure that the collected data is of high quality and semantic accuracy, which is crucial for the validity of research results.
The ability to adapt archetypes to specific research needs while maintaining interoperability is a key advantage.
In addition, the Archetype Query Language (AQL) enables researchers to submit precise and complex queries to the openEHR databases in order to extract the information they need for their studies.
openEHR facilitates the reuse of data already collected in clinical care for research purposes (secondary use). The standardized structure and semantic interoperability of openEHR data significantly reduce the effort required for data preparation and integration.
Projects such as HiGHmed in Germany demonstrate how heterogeneous data from different hospitals can be consolidated in an openEHR platform to provide standardized data for important research projects in areas such as cardiology, oncology, and infection control. The ability to aggregate and query data from different sources in a uniform format opens up new avenues for translational research and the generation of insights from real-world healthcare data.
The integration of openEHR
with other research data models such as the OMOP Common Data Model (CDM) is also being actively pursued in order to increase the usability of openEHR data for a broader research audience. Several research projects worldwide are already successfully using openEHR. For example, the University of Auckland in New Zealand is conducting research on the maintainability and interoperability of software using openEHR. In China, a case study was conducted on the application of openEHR archetypes in a clinical data repository. The COVID-19 pandemic has demonstrated the flexibility of openEHR by enabling the rapid development of templates for various research purposes. These examples underscore the potential of openEHR to increase the efficiency and quality of medical research through improved data management and utilization.
What are the chances?
The introduction of openEHR is not without its challenges. The initial complexity of the specifications and the associated learning curve for developers and clinical modelers can be a hurdle. Migrating existing systems and data can also be a time-consuming and demanding task.
Careful planning, comprehensive training, and possibly the support of experienced experts are required to overcome these initial difficulties.
Nevertheless, the long-term benefits of openEHR far outweigh the initial challenges.
Improved interoperability, future-proof data, and the system's high flexibility create a solid foundation for high-quality, efficient healthcare. Building an open, standardized data platform can lead to long-term cost savings and promote innovation in healthcare.
The separation of data and applications enables more agile development and the integration of new technologies such as artificial intelligence (AI) into healthcare. Although the initial implementation of openEHR may seem complex, it offers the opportunity to create a future-proof and interoperable IT infrastructure in healthcare that meets the needs of patients and healthcare providers alike.