openEHR Architecture Overview

This document provides an overview of the openEHR architecture in terms of a model overview, key global semantics, deployment and integration architectures, relationship to published standards, and finally the approach to building Implementation Technology Specifications (ITSs). Semantics specific to each information, archetype and service model are described in the relevant specification.

The intended audience includes:

This document is the key technical overview of openEHR, and should be read before all other technical documents.

This specification is in the STABLE state. The development version of this document can be found at https://specifications.openehr.org/releases/BASE/Release-1.0.2/architecture_overview.html.

Known omissions or questions are indicated in the text with a 'to be determined' paragraph, as follows:

Feedback may be provided on the technical mailing list.

Issues may be raised on the specifications Problem Report tracker.

To see changes made due to previously reported issues, see the BASE component Change Request tracker.

FIGURE 1 illustrates the openEHR Specification Project. This project is responsible for developing the specifications on which the openEHR Health Computing Platform is based. The relationships between the parts of the computing platform and the specifications are indicated on the diagram. The project deliverables include requirements, abstract specifications, implementation technology specifications (ITSs), computable expressions and conformance criteria.

The abstract specifications consist of the Reference Model (RM), the Service Model (SM) and Archetype Model (AM). The first two correspond to the ISO RM/ODP information and computational viewpoints respectively. The latter formalises the bridge between information models and knowledge resources.

The abstract specifications published by openEHR are defined using the UML notation and formal textual class specifications. These models constitute the primary references for all openEHR semantics. The presentation style of these abstract specifications is deliberately intended to be clear and semantically close to the ideas being communicated. Accordingly, the specifications do not follow idioms or limitations of particular programming languages, schema languages or other formalisms.

The specifications are also available in a tool-oriented computable UML format in order to enable development of software and systems. The computable expressions for all practical purposes can be assumed as being a lossless rendition of the published abstract specifications.

The implementation technology specifications on the other hand correspond to the expression of abstract specifications in various programming and schema languages, each of which represents an imperfect and usually partial transformation from the specification models. There are numerous implementation technologies, ranging from programming languages, serial formalisms such as XML, to database and distributed object interfaces. Each of these has its own limits and strengths. The approach to implementing any of the openEHR abstract models in a given implementation technology is to firstly define an ITS for the particular technology, then to use it to formally map the abstract models into expressions in that technology.

This section provides a brief overview of the requirements underpinning the openEHR architecture. The openEHR architecture embodies 15 years of research from numerous projects and standards from around the world. It has been designed based on requirements captured over many years.

Because the architecture is highly generic, and particularly due to being archetype-driven, it satisfies many requirements outside the original concept of the "clinical EHR". For example, the same reference architecture can be used for veterinary health or even "care" of public infrastructure or listed buildings. This is due to the fact that the reference model embodies only concepts relating to "service and administrative events relating to a subject of care"; it is in archetypes and templates that specifics of the kinds of care events and subjects of care are defined. In another dimension, although one of the requirements for the openEHR EHR was a "patient-centric, longitudinal, shared care EHR", it is not limited to this, and can be used in purely episodic, specialist situations, e.g. as a radiology department record system. Requirements for various flavours of "health care record" can be categorised according to the two dimensions, scope, and kind of subject, as shown below.

In this figure, each bubble represents a set of requirements, being a superset of all requirements of bubbles contained within it. Requirements for a generic record of care for any kind of subject in a local deployment are represented by the top left bubble. The subsequent addition of requirements corresponding to living subjects and then human subjects is represented by the bubbles down the left side of the diagram. The requirements represented by the largest bubble on the left hand side correspond to "local health records for human care", such as radiology records, hospital EPRs and so on. Additional sets of requirements represented by wider bubbles going across the diagram correspond to extending the scope of the content of the care record first to a whole subject (resulting in a patient-centric, longitudinal health record) and then to populations or cohorts of subjects, as done in population health and research. From the (human) healthcare point of view, the important requirements groups extend all the way to the bottom row of the diagram.

Going down the diagram, requirements corresponding to increasing specificity of subject of care (from "any" to "human") are mostly implemented in openEHR by the use of archetypes. Going across the diagram, the requirements corresponding to increasing scope of record content (from episodic to population) are mainly expressed in different deployments, generally going from standalone to a shared interoperable form. One of the key aspirations for EHRs today is the "integrated care record" sought by many health authorities today (see [ISO_20514] for definition of ICEHR), which provides an informational framework for integrated shared care.

As a result of the approach taken by openEHR, components and applications built to satisfy the requirements of an integrated shared care record can also be deployed as (for example) an episodic radiology record system.

Some of the key requirements developed during the evolution of GEHR to openEHR are listed in the following sections, corresponding to some of the major requirements groups of the figure Structure of Requirements met by openEHR.

From a more specifically clinical care perspective (rather than a record-keeping perspective), the following requirements have been identified during the development of openEHR:

The Integrated Care EHR holds great promise: to generalise and make widely available the benefits of computerisation that have been demonstrated individually and in isolated settings. These can be summarised as:

One comprehensive statement of EHR requirements covering many of the above is the ISO Technical Report 18308 [ISO_18308] for which an openEHR profile has been created [openehr_18308]. The requirements summarised above are described in more detail in the openEHR EHR Information Model document.

Ultimately any software and information architecture only provides utility when deployed. The architecture of openEHR is designed to support the construction of a number of types of system. One of the most important, the integrated shared care health record is illustrated in the figure below.

In this form, the openEHR services are added to the existing IT infrastructure to provide a shared, secure health record for patients that are seen by any number of health providers in their community context. openEHR-enabled systems can also be used to provide EMR/EPR functionality at provider locations. Overall, a number of important categories of system can be implemented using openEHR including the following:

The most basic kind of distinction in any system of models is ontological, i.e. in the levels of abstraction of description of the real world. All models carry some kind of semantic content, but not all semantics are the same, or even of the same category. For example, some part of the SNOMED-CT [SNOMED_CT] terminology describes types of bacterial infection, sites in the body, and symptoms. An information model might specify a logical type Quantity. A content model might define the model of information collected in an ante-natal examination by a physician. These types of "information" are qualitatively different, and need to be developed and maintained separately within the overall model eco-system. The figure below illustrates these distinctions, and indicates what parts are built directly into software and databases.

This figure shows a primary separation between "ontologies of information" i.e. models of information content, and "ontologies of reality" i.e. descriptions and classifications of real phenomena. These two categories have to be separated because the type of authors, the representation and the purposes are completely different. In health informatics, this separation already exists by and large, due to the development of terminologies and classifications.

A secondary ontological separation within the information side is shown between information models and domain content models. The former category corresponds to semantics that are invariant across the domain (e.g. basic data types like coded terms, data structures like lists, identifiers), while the latter corresponds to variable domain level content descriptions - descriptions of information structures such as "microbiology result" rather than descriptions of actual phenomena in the real world (such as infection by a microbe). This separation is not generally well understood, and historically, a great deal of domain-level semantics has been hard-wired into the software and databases, leading to relatively unmaintainable systems.

By clearly separating the three categories - information models, domain content models, and terminologies - the openEHR architecture enables each to have a well-defined, limited scope and clear interfaces. This limits the dependence of each on the other, leading to more maintainable and adaptable systems.

A second key design paradigm used in openEHR is that of separation of responsibilities within the computing environment. Complex domains are only tractable if the functionality is first partitioned into broad areas of interest, i.e. into a "system of systems" [6]. This principle has been understood in computer science for a long time under the rubrics "low coupling", "encapsulation" and "componentisation", and has resulted in highly successful frameworks and standards, including the OMG’s CORBA specifications and the explosion of object-oriented languages, libraries and frameworks. Each area of functionality forms a focal point for a set of models formally describing that area, which, taken together usually correspond to a distinct information system or service.

The following illustrates a notional health information environment containing numerous services, each denoted by a bubble. Typical connections are indicated by lines, and bubbles closer to the centre correspond to services closer to the core needs of clinical care delivery, such as the EHR, terminology, demographics/identification and medical reference data. Of the services shown on the diagram, openEHR currently provides specifications only for the more central ones, including EHR and Demographics.

Since there are standards available for some aspects of many services, such as terminology, image formats, messages, EHR Extracts, service-based interoperation, and numerous standards for details such as date/time formats and string encoding, the openEHR specifications often act as a mechanism to integrate existing standards.

The third computing paradigm used in openEHR is a natural consequence of the separation of responsibilities, namely the separation of viewpoints. When responsibilities are divided up among distinct components, it becomes necessary to define a) the information that each processes, and b) how they will communicate. These two aspects of models constitute the two central "viewpoints" of the ISO RM/ODP model [4], marked in bold in the following:

The openEHR specifications accordingly include an information viewpoint - the openEHR Reference Model - and a computational viewpoint - the openEHR Service Model. The Engineering viewpoint corresponds to the Implementation Technology Specification (ITS) models of openEHR (see Implementation Technology Specifications), while the Technological viewpoint corresponds to the technologies and components used in an actual deployment. An important aspect of the division into viewpoints is that there is generally not a 1:1 relationship between model specifications in each viewpoint. For example, there might be a concept of "health mandate" (a CEN ENV13940 Continuity of Care concept) in the enterprise viewpoint. In the information viewpoint, this might have become a model containing many classes. In the computational viewpoint, the information structures defined in the information viewpoint are likely to recur in multiple services, and there may or may not be a "health mandate" service. The granularity of services defined in the computational viewpoint corresponds most strongly to divisions of function in an enterprise or region, while the granularity of components in the information view points corresponds to the granularity of mental concepts in the problem space, the latter almost always being more fine-grained.

The following figure illustrates the package structure of the openEHR formal specifications. Three major packages are defined: rm, am and sm. All packages defining detailed models appear inside one of these outer packages, which may also be thought of as namespaces. They are conceptually defined within the org.openehr namespace, which can be represented in UML as further packages. In some implementation technologies (e.g. Java), the org.openehr namespace may actually be used within program texts.

One of the important design aims of openEHR is to provide a coherent, consistent and re-usable type system for scientific and health computing. Accordingly, the ‘core’ of the RM (bottom-most layers) provides identifiers, data types, data structures and various common design patterns that can be reused ubiquitously in the upper layers of the RM, and equally in the AM and SM packages. The figure below illustrates the relationships between the key packages. Dependencies only exist from higher packages to lower packages.

Each package defines a local context for definition of classes. The figure below illustrates the RM package structure. An informal division into "domain", "patterns" and "core" is shown. The packages in the latter group are generic, and are used by all openEHR models, in all the outer packages. Together, they provide identification, access to knowledge resources, data types and structures, versioning semantics, and support for archetyping. The packages in the former group define the semantics of enterprise level health information types, including the EHR and demographics.

Each outer package in the figure Structure of org.openehr.rm package corresponds to one openEHR specification document (with the exception of the EHR and Composition packages, which are both described in the EHR Reference Model document), documenting an "information model" (IM). The package structure will normally be replicated in all ITS expressions, e.g. XML schema, programming languages like Java, C# and Eiffel, and interoperability definitions like WSDL, IDL and .Net.

The openEHR am package contains the models necessary to describe the semantics of archetypes and templates, and their use within openEHR. These include ADL, the Archetype Definition Language (expressed in the form of a syntax specification), the archetype and template packages, defining the object-oriented semantics of archetypes and templates, and the openehr_profile package, which defines a profile of the generic archetype model defined in the archetype package, for use in openEHR (and other health computing endeavours). The internal structure of the am package is shown below.

The openEHR service model includes definitions of basic services in the health information environment, centred around the EHR. It is illustrated in the figure below. The set of services actually included will undoubtedly evolve over time, so this diagram should not be seen as definitive.

In informational terms, a minimal EHR system based on openEHR consists of an EHR repository, an archetype repository, terminology (if available), and demographic/identity information, as shown below.

The latter may be in the form of an existing PMI (patient master index) or other directory, or it may be in the form of an openEHR demographic repository. An openEHR demographic repository can act as a front end to an existing PMI or in its own right. Either way it performs two functions: standardisation of demographic information structures and versioning. An openEHR EHR contains references to entities in whichever demographic repository has been configured for use in the environment; the EHR can be configured to include either no demographic or some identifying data. One of the basic principles of openEHR is the complete separation of EHR and demographic information, such that an EHR taken in isolation contains little or no clue as to the identity of the patient it belongs to. The security benefits are described below. In more complete EHR systems, numerous other services (particularly security-related) would normally be deployed, as shown in figure A Health Information Environment.

As has been shown, the openEHR information models define information at varying levels of granularity. Fine-grained structures defined in the Support and Data types are used in the Data Structures and Common models; these are used in turn in the EHR, EHR Extract, Demographic and other "toplevel" models. These latter models define the "top-level structures" of openEHR, i.e. content structures that can sensibly stand alone, and may be considered the equivalent of separate documents in a document-oriented system. In openEHR information systems, it is generally the top-level structures that are of direct interest to users. The major top-level structures include the following:

All persistent openEHR EHR, demographic and related content is found within top-level information structures. Most of these are visible in the following figures.

The openEHR EHR is structured according to a relatively simple model. A central EHR object identified by an EHR id specifies references to a number of types of structured, versioned information, plus a list of Contribution objects that act as audits for changes made to the EHR. The high-level structure of the openEHR EHR is shown below.

In this figure, the parts of the EHR are as follows:

The internal structure of the Composition along with the Directory object correspond closely to the levels in internationally agreed models of health information such as the CEN EN13606 and HL7 CDA standards.

The logical structure of a typical Composition is shown in more detail in the next figure. This shows various hierarchical levels from Composition to the data types are shown in a typical arrangement. The 21 data types provide for all types of data needed for clinical and administrative recording.

A key part of the investigation process shown in figure [clinical_investigator_process], and indeed healthcare in general, is intervention. Specifying and managing interventions (whether the simplest prescriptions or complex surgery and therapy) is a hard problem for information systems because it is in "future time" (meaning that intervention activities have to be expressed using branching/looping time specifications, not the simple linear time of observations), unexpected events can change things (e.g. patient reaction to drugs), and the status of a given intervention can be hard to track, particularly in distributed systems. However, from the health professional’s point of view, almost nothing is more basic than wanting to find out: what medications is this patient on, since when, and what is the progress? The openEHR approach to these challenges is to use the Entry type INSTRUCTION, its subpart ACTIVITY to specify interventions in the future, and the Entry subtype ACTION to record what has actually happened. A number of important features are provided in this model, including:

Coupled with the comprehensive versioning capabilities of openEHR, the Instruction/Action design allows clinical users of the record to define and manage interventions for the patient in a distributed environment.

Time is well-known as a challenging modelling problem in health information. In openEHR, times that are a by-product of the investigation process (e.g. time of sampling or collection; time of measurement, time of a healthcare business event, time of data committal) described above are concretely modelled, while other times specific to particular content (e.g. date of onset, date of resolution) are modelled using archetyping of generic data attributes. The following figure shows a typical relationship of times with respect to the observation process, and the corresponding attributes within the openEHR reference model. Note that under different scenarios, such as GP consultation, radiology reporting and others, the temporal relationships may be quite different than those shown in the figure. Time is described in detail in the EHR Information Model [openehr_rm_ehr].

In some situations, there may be more than one language used in the EHR. This may be due to patients being treated across borders (common among the Scandinavian countries, between Brazil and northern neighbours), or due to patients being treated while travelling, or due to multiple languages simply being used in the home environment.

Language is handled as follows in the openEHR EHR. The default language for the whole EHR is determined from the operating system locale. It may be included in the EHR_status object if desired. Language is then mandatorily indicated in two places in the EHR data, namely in Compositions and Entries (i.e. Observations, etc), in a language attribute. This allows both Compositions of different languages in the EHR, and Entries of different languages in the same Composition. Additionally, within Entries, text and coded text items may optionally have language recorded if it is different from the language of the enclosing Entry, or where these types are used within other non-Entry structures that don’t indicate language.

The use of these features is mostly likely to occur due to translation, although in some cases a truly multi-lingual environment might exist within the clinical encounter context. In the former case, some parts of an EHR, e.g. particular Compositions will be translated before or after a clinical encounter to as to make the information available in the primary language of the EHR. The act of translation (like any other interaction with the EHR) will cause changes to the record, in the form of new Versions. New translations can conveniently be recorded as branch versions, attached to the version of which they are a translation. This is not mandatory, but provides a convenient way to store translations so that they don’t appear to replace the original content.

Any model of how security and privacy are supported in the health record must be based on some notion of assumed threats. Without going into great detail, security threats assumed by openEHR include the following (here "inappropriate" means anything that is not or would not be consented to by the patient):

A key principle with respect to the design of mechanisms supporting security, confidentiality and integrity has to be kept in mind: the likelihood of any given mode of targeted inappropriate access is proportional to the perceived value of the information and inversely proportional to the cost of access. To paraphrase Ross Anderson’s BMA paper [Anderson_1996] on health data security, for a given access, the perpretrator will try to find the simplest, cheapest and quickest method, which is more likely to be bribery or burglary than James Bond-inspired technology. openEHR makes use of this principle by providing some relatively simple mechanisms that are cheap to implement but can make misuse quite difficult, without compromising availability.

Version control is an integral part of the openEHR architecture. An openEHR repository for EHR or demographic information is managed as a change-controlled collection of "version containers" (modelled by the VERSIONED_OBJECT<T> class in the common.change_control package), each containing the versions of a top-level content structure (such as a Composition or Party) as it changes over time. A version-controlled top-level content structure is visualised below.

Versioning of single top-level structures is a necessary, but not sufficient requirement for a repository that must provide coherence, traceability, indelibility, rollback, and support for forensic examination of past states of the data. Features supporting "change control" are also required. Under a disciplined change control scheme, changes are not made arbitrarily to single top-level structures, but to the repository itself. Changes take the form of change-sets, called "Contributions", that consist of new or changed versions of the controlled items in the repository. The key feature of a change-set is that it acts like a transaction, and takes the repository from one consistent state to another, whereas arbitrary combinations of changes to single controlled items could easily be inconsistent, and even dangerously wrong where clinical data are concerned.

These concepts are well-known in configuration management (CM), and are used as the basis for most software and other change management systems, including numerous free and commercial products available today. They are a central design feature of openEHR architecture. The following sections provide more details

The "configuration management" (CM) paradigm is well-known in software engineering, and has its own IEEE standard [IEEE_828]. CM is about managed control of changes to a repository of items (formally called "configuration items" or CIs), and is relevant to any logical repository of distinct information items which changes in time. In health information systems, at least two types of information require such management: electronic health records, and demographic information. In most analyses in the past, the need for change management has been expressed in terms of specific requirements for audit trailing of changes, availability of previous states of the repository and so on. In openEHR, the aim is to provide a formal, general-purpose model for change control, and show how it applies to health information.

Properly managing changes to the repository requires two mechanisms. The first, version control, is used to manage versions of each CI, and of the directory structure if there is one. The second is the concept of the "change-set", known as a contribution in openEHR. This is the set of changes to individual CIs (and other top-level structures in the EHR) made by a user as part of some logical change. For example, in a document repository, the logical change might be an update to a document that consists of multiple files (CIs). There is one Contribution, consisting of changes to the document file CIs, to the repository. In the EHR, a Contribution might consist of changes to more than one Composition, and possibly to the organising Folder structure. Any change to the EHR requires a Contribution. The kinds of changes that can occur to items affected in a Contribution are:

A typical sequence of changes to a repository is illustrated in the following figure.

This shows the effect of four Contributions (indicated by blue ovals on the left hand side) to a repository containing a number of CIs (the directory tree is not shown for the sake of simplicity). As each Contribution is made, the repository is changed in some way. The first brings into existing a new CI, and modifies three others (changes indicated by the ‘C’ triangles). The second Contribution causes the creation of a new CI only. The third causes a creation as well as two changes, while the fourth causes only a change. (Changes to the folder structure are not shown here).

One nuance which should be pointed out is that in the figure above Contributions are shown as if they are literally a set of deltas, i.e. exactly the changes which occur to the record. Thus, the first Contribution is the set {CI_w, C_a1, C_c1, C_d1} and so on. Whether this is literally true depends on the construction of the persistence solution. In some situations, some CIs may be updated by the user viewing the current list and entering just the changes - the situation shown above; in others, the system may provide the current state of these CIs for editing by the user, and submit the updated versions, as shown in the next figure. Some applications may do both, depending on which CI is being updated. The internal versioning implementation may or may not generate deltas as a way of efficient storage.

For the purposes of openEHR, a Contribution is considered as being the set of Versions created or attested at one time, as implied by the figure above.

An underlying design concept of the versioning model defined in openEHR is known as a "virtual version tree". The idea is simple in the abstract. Information is committed to a repository (such as an EHR) in lumps, each lump being the "data" of one Version. Each Version has its place within a version tree, which in turn is maintained inside a Versioned object (or "version container"). The virtual version tree concept means that any given Versioned object may have numerous copies in various systems, and that the creation of versions in each is done in such a way that all versions so created are in fact compatible with the "virtual" version tree resulting from the superimposition of the version trees of all copies. This is achieved using simple rules for version identification and is done to facilitate data sharing. Two very common scenarios are served by the virtual version tree concept:

The versioning scheme used in openEHR guarantees that no matter where data are created or copied, there are no inconsistencies due to sharing, and that logical copies are explicitly represented. It therefore provides direct support for shared data in a shared care context.

In an openEHR system, each EHR has a unique identifier, known as the EHR id, found in the root EHR object of each EHR. EHR ids should be "strong" globally unique identifiers such as reliably created Oids or Guids. No single system should contain two EHRs for the same subject. If this is not the case, it means that the EHR system has failed to detect the existence of an EHR for a subject, or failed to match provided demographic attributes to the subject.

In a distributed environment, the correspondence of EHR ids to subjects (i.e. patients) is variable, and depends on the level of integration of the environment. In non-integrated or sporadically connected environments, the same patient is likely to have a separate EHR at each institution, each with its own unique EHR id, but the same subject id. If copies of parts of the patient EHR at one location is requested by another provider, the received items will be merged into the local EHR for that patient. Merges of persistent Compositions in such circumstances are likely to require human intervention. Multiple EHR ids per patient in a distributed context are evidence of a lack of systematic connectivity or identification service.

In a fully integrated distributed environment, the typical patient will still have local EHRs in multiple locations, but each carrying the same EHR id. When a patient presents in a new location, a request to the environment’s identification service could be made to determine if there is already an EHR for this patient. If there is, a clone of all or part of the existing EHR could be made, or a new empty EHR could be created, but in all cases, the EHR id would be the same as that used in other locations for the same patient.

Note that the above logic only holds where the EHR in each location is an openEHR EHR.

Under the two-level modelling approach, the formal definition of information structuring occurs at two levels. The lower level is that of the reference model, a stable object model from which software and data can be built. Concepts in the openEHR reference model are invariant, and include things like Composition, Section, Observation, and various data types such as Quantity and Coded text. The upper level consists of domain-level definitions in the form of archetypes and templates. Concepts defined at this level include things such as "blood pressure measurement", "SOAP headings", and "HbA1c Result".

All information conforming to the openEHR Reference Model (RM) - i.e. the collection of Information Models (IMs) - is "archetypable", meaning that the creation and modification of the content, and subsequent querying of data is controllable by archetypes. Archetypes are themselves separate from the data, and are stored in their own repository. The archetype repository at any particular location will usually include archetypes from well-known online archetype libraries. Archetypes are deployed at runtime via templates that specify particular groups of archetypes to use for a particular purpose, often corresponding to a screen form.

Archetypes are themselves instances of an archetype model, which defines a language in which to write archetypes; the syntax equivalent of the model is the Archetype Definition Language, ADL. These formalisms are specified in the openEHR Archetype Object Model (AOM) and ADL documents respectively. Each archetype is a set of constraints on the reference model, defining a subset of instances that are considered to conform to the subject of the archetype, e.g. "laboratory result". An archetype can thus be thought of as being similar to a LEGO® instruction sheet (e.g. for a tractor) that defines the configuration of LEGO® bricks making up a tractor. Archetypes are flexible; one archetype includes many variations, in the same way that a LEGO® instruction might include a number of options for the same basic object. Mathematically, an archetype is equivalent to a query in F-logic [Kifer_Lausen_Wu_1995].

In terms of scope, archetypes are general-purpose, re-usable, and composable. For data capture and validation purposes, they are usually used at runtime by templates. An openEHR Template is a specification that defines a tree of of one or more archetypes, each constraining instances of various reference model types, such as Composition, Section, Entry subtypes and so on. Thus, while there are likely to be archetypes for such things as "biochemistry results" (an Observation archetype) and "SOAP headings" (a Section archetype), templates are used to put archetypes together to form whole Compositions in the EHR, e.g. for "discharge summary", "antenatal exam" and so on. Templates usually correspond closely to screen forms, printed reports, and in general, complete application-level lumps of information to be captured or sent; they may therefore be used to define message content. They are generally developed and used locally, while archetypes are usually widely used.

A template is used at runtime to create default data structures and to validate data input, ensuring that all data in the EHR conform to the constraints defined in the archetypes referenced by the template. In particular, it conforms to the path structure of the archetypes, as well as their terminological constraints. Which archetypes were used at data creation time is written into the data, in the form of both archetype identifiers at the relevant root nodes, and archetype node identifiers (the [atnnnn] codes), which act as normative node names, and which are in turn the basis for paths. When it comes time to modify the same data, these archetype node identifiers enable applications to retrieve and use the original archetypes, ensuring modifications respect the original constraints.

Archetypes also form the basis of semantic querying. Queries are expressed in a language which is a synthesis of SQL (SELECT/FROM/WHERE) and W3C XPaths, extracted from the archetypes.

All nodes within the top-level information structures in the openEHR RM are "archetypable", with certain nodes within those structures being archetype "root points". Each top-level type is always guaranteed to be an archetype root point. Although it is theoretically possible to use a single archetype for an entire top-level structure, in most cases, particularly for COMPOSITION and PARTY, a hierarchical structure of multiple archetypes will be used, via the slot mechanism described above. This allows for componentisation and reusability of archetypes. When hierarchies of archetypes are used for a top-level structure, there will also be archetype root points in the interior of the structure. For example, within a COMPOSITION, ENTRY instances (i.e. OBSERVATIONs, EVALUATIONs etc.) are almost always root points. SECTION instances are root points if they are the top instance in a Section structure; similarly for FOLDER instances within a directory structure. Other nodes (e.g. interior SECTIONs, ITEM_STRUCTURE instances) might also be archetype root points, depending on how archetypes are applied at runtime to data. The following figure illustrates the application of archetypes and templates to data.

Archetype-enabling of Reference Model classes is achieved via inheritance of the class LOCATABLE from the package common.archetyped (see Common IM). The LOCATABLE class includes the attributes archetype_node_id and archetype_details. In the data, the former carries an identifier from the archetype. If the node in the data is a root point, it carries the multipart identifier of the generating archetype, and archetype_details carries an ARCHETYPED object, containing information pertinent to archetype root points. If it is a non-root node, the archetype_node_id attribute carries the identifier (known as an "at", or "archetype term" code) of the archetype interior node that generated the data node, and the archetype_details attribute is void.

Sibling nodes in data can carry the same archetype_node_id in some cases, since archetypes provide a pattern for data, rather than an exact template. In other words, depending on the archetype design, a single archetype node may be replicated in the data.

In this way, each archetyped data composition in openEHR data has a generating archetype which defines the particular configuration of instances to create the desired composition. An archetype for "biochemistry results" is an OBSERVATION archetype, and constrains the particular arrangement of instances beneath an OBSERVATION object; a "problem/SOAP headings" archetype constrains SECTION objects forming a SOAP headings structure. In general, an archetyped data composition is any composition of data starting at a root node and continuing to its leaf nodes, at which point lower-level compositions, if they exist, begin. Each of the archetyped areas and its subordinate archetyped areas in figure [archetypes_and_data] is an archetyped data composition.

The result of the use of archetypes to create data in the EHR (and other systems) is that the structure of data in any top-level object conforms to the constraints defined in a composition of archetypes chosen by a template, including all optionality, value, and terminology constraints.

The use of openEHR archetypes and templates enables paths to be used ubiquitously in the openEHR architecture. Paths are extracted from Archetypes and templates, and are constructed from attribute names and archetype node identifiers, in an Xpath-compatible syntax, as shown in the following figure. These paths serve to identify any node in a template or archetype, such as the "diastolic blood pressure" ELEMENT node, deep within a "blood pressure measurement" archetype. Since archetype node identifiers are embedded into data at runtime, archetype paths can be used to extract data nodes conforming to particular parts of archetypes, providing a very powerful basis for querying. Paths can also be constructed into data, using more complex predicates (still in the Xpath style). Paths in openEHR are explained in detail under Paths and Locators on page 57.

A set of heavily reviewed archetypes is available on the openEHR Cinical Knowledge Manager (CKM). This collection is authored by hundreds of clinical professionals, and is constantly growing.

The openEHR architecture includes a path mechanism that enables any node within a top level structure to be specified from the top of the structure using a "semantic" (i.e. archetype-based) X-path compatible path. The availability of such paths radically changes the available querying possibilities with health information, and is one of the major distinguishing features of openEHR.

Technically, the combination of a path and a Version identifier such as OBJECT_VERSION_ID forms a "globally qualified node reference" which can be expressed using LOCATABLE_REF. It can also be expressed in portable URI form as a DV_EHR_URI, known as a "globally qualified node locator". Either representation enables any openEHR data node to be referred to from anywhere. This section describes the syntax and semantics of paths, and of the URI form of reference. In the following, the term "archetype path" means a path extracted from an archetype, while "data path" means one that identifies an item in data. They are no different formally, and this terminology is only used to indicate where they are used.

There are two broad categories of URIs that can be used with any resource: direct references, and queries. The first kind are usually generated by the system containing the referred-to item, and passed to other systems as definitive references, while the second are queries from the requesting system in the form of a URI.

openEHR archetypes provide a powerful way to define the meaning of clinical and related data, and to connect, or "bind", data to recognised terminologies such as LOINC, ICDx, ICPC, SNOMED-CT and the many other terminologies and vocabularies used in healthcare. Terminology is used in openEHR in the following ways:

The following sections describe these features.

openEHR has its own small terminology and code sets, which are used to provide the value sets of a number of attributes in the reference model. Code sets are used to express well-known internationally standardised lists of codes where the codes themselves have meaningful values e.g. the ISO 3166 country codes ("au", "cn", "pl" etc). Six such code sets are used by various attributes in the reference model, each of type CODE_PHRASE (the openEHR type used to represent a term code). For other coded attributes, such as PARTICIPATION.function in the reference model, the openEHR terminology takes the more orthodox route in terminology design, and defines value sets in groups using meaningless codes and rubrics. These attributes are always of type DV_CODED_TEXT; the code itself is contained within the defining_code attribute.

The openEHR terminology is described in the openEHR Terminology specification [openehr_terminology], with computable expressions available at the openEHR terminology page [openehr_terminology_resources].

Archetypes contain their own local terminology (found in the 'ontology' section of an archetype). The use of internal term sets is appropriate when there is no structure to the terms (ie no relationships) and when synonyms are not important. Thus, the use is limited to small flat lists of terms. The advantages of the terms being internal to the archetype, apart from computational efficiency mentioned above, are:

It is clear, however, that many archetypes require a connection to external terminologies to provide the full benefits of automatic processing; this is described in the next section. The internal terminology takes the form of a set of {code, text, description} semantic definitions for each node of the archetype structure. Each such term is identified by an "at" (archetype term) code, e.g. [at0012]. Each code defined locally in an archetype is used for one of two purposes:

For example, the local codes in an "Apgar result" archetype could contain terms for "1 minute event" and "2 minute event". These codes are associated with the reference model nodes within the 'definition' part of the archetype. In the Apgar example, the two codes (say [at0003] and [at0026]) will be mapped to nodes of reference model type EVENT (rm.data_structures.history package), as shown below. It is this mapping that is the basis for archetype paths: an archetype path is simply the alternating pattern of reference model attribute names and node codes.

The second use of local codes is as values. Above, the ELEMENT node identified by code [at0005] has as its value constraint an ORDINAL type whose values can be 0, 1, or 2. Each of these values is coded by the codes [at0006], [at0007], and [at0008]. An extract of the archetype ontology showing these terms is shown below.

Querying through EHR data is frequently cited to be the major utility of terminology with respect to health information. With the mappings defined in archetypes, a number of approaches are possible, however the semantics of the intended query need to be understood first. Consider a query for "adenocarcinoma" on a patient record. SNOMED-CT includes 63 terms beginning with "adenocarcinoma" (and 171 terms which include the word as a secondary part of the phrase), some as children of a common parent. Nevertheless, the terms do not all have a single common parent; a choice has to be made of which terms correspond to the intent of the query. If it is to find any previous diagnosis of "adenocarcinoma", then at least the terms of the form [snomed-ct::254626006|adenocarcinoma of lung|], "…​ of liver" have to be included. These are within the "clinical finding" hierarchy, so the use of these latter terms should ensure that matches are not made with other uses of the same terms in the record, e.g. "fear of adenocarcinoma" or "minimal risk of adenocarcinoma". Such correct matching is completely dependent upon the correct use of SNOMED-CT terms in the first place by the software application and/or user creating the data. It is easy to imagine an application that saves data (including openEHR data) in the form of two name/value pairs: <"principal diagnosis", [snomed-ct::35917007|adenocarcinoma|])> and <"site", "lung">. Querying using [snomedct::254626006|adenocarcinoma of lung|] will fail, even though this is exactly the meaning of the data. The data are not wrong as such, but the lesson is clear: coding of data and code use in queries must be governed by common models, otherwise there is no hope of reliably processing the data.

Under the openEHR aproach, path-based querying can be used to specify (for example):

The assumption here is that the value at this path was originally restricted by the archetype from which the path is taken, to conforming to the relation {is-a "clinical finding" and is-a "abnormal morphological mass"}. Any finding of adenocarcinoma of the lung is then forced to be from the resulting subsumption hierarchy; other "adenocarcinoma" terms cannot be wrongly used in this position.

However, even if the archetype had not restricted the value in this way, the same query which searched for any "adenocarcinoma" term at the same path could reasonably be used to locate "previous diagnoses of adenocarcinoma", since this is the only use of the archetype. In a similar way, archetype path-based querying can be used to distinguish the other potential ambiguities described in the section on [Entries and clinical statements].

Previous sections have described the software architecture of the openEHR specifications. Here we describe how the package architecture can be applied to building real systems. The general architectural approach in any openEHR system can be considered as 5 layers (i.e. a "5-tier" architecture). The tiers are as follows.

The same tiers can be used in large deployments, as shown in the next figure or simply as layers in single- machine applications.

The figure below illustrates an approximate mapping of major parts of the openEHR software architecture to the 5-tier scheme. Clearly where parts of the architecture are used will depend on various implementation choices; the mapping shown is therefore not definitive. Nevertheless, the principal use of parts of the architecture is likely to be similar in most systems, as follows:

Some will base the GUI code on them, while others will have either tool-generate code, or dynamically generate forms based on particular templates and archetypes. In the future, an abstract persistence API and optimised persistence models (transformations of the existing RM models) are likely to be published by openEHR in order to help with the implementation of databases.

Getting data in and out of the EHR is one of the most basic requirements openEHR aims to satisfy. In "greenfield" (new build) situations, and for data being created by GUI applications via the openEHR EHR APIs, there is no issue, since native openEHR structures and semantics are being used. In almost all other situations, existing data sources and sinks have to be accounted for. In general, external or ‘legacy’ data (here the term is used for convenience, and does not imply anything about the age or quality of the systems in question) have different syntactic and semantic formats than openEHR data, and seamless conversion requires addressing both levels.

Existing data sources and sinks include relational databases, HL7v2 messages, HL7 CDA documents and are likely to include CEN EN13606 data. HL7v2 messages are probably one of the most common sources of pathology messages in many countries; EDIFACT messages are another. More recently, HL7v2 messages have been designed for referrals and even discharge summaries. Not all legacy systems are standardised; most hospital and GP products have their own private models of data and terminology usage.

The primary need with respect to legacy data is to be able to convert data from multiple mutually incompatible sources into a single, standardised patient-centric EHR for each patient, that can then be longitudinally viewed and queried. This is what enables GP and specialist notes, diagnoses and plans to be integrated with laboratory results from multiple sources, patient notes, administrative data and so on, to provide a coherent record of the patient journey.

In technical terms, a number of types of incompatibility have to be dealt with. There is no guarantee of correspondence of scope of incoming transactions and target openEHR structures - an incoming document for example might correspond to a number of clinical archetypes. Structure will not usually correspond, with legacy data (particularly messages) usually having flatter structures than those defined in target archetypes. Terminology use is extremely variable in existing systems and messages, and also has to be dealt with. Data types will also not correspond directly, so that for example, a mapping between an incoming string "110/80 mmHg" and the target openEHR form of two DV_QUANTITY objects each with their own value and units has to be made.

The foundation of a key approach to the integration problem is the use of two kinds of archetypes. So far in this document "archetypes" has meant "designed" archetypes, generally clinical, demographic or administrative. The common factors for all such archetypes are:

A second category of archetypes is "integration" archetypes. These are characterised as follows:

In the data integration environment, "designed" archetypes always define the target structures, coding and other semantics of data, while "integration" archetypes provide the means mapping of external data into the openEHR environment.

The integration archetype-based strategy for importing data into an openEHR system, shown below, consists of two steps.

Firstly, data are converted from their original syntactic format into openEHR COMPOSITION / SECTION / GENERIC_ENTRY structures, shown in the openEHR integration switch. Most of the data will appear in the GENERIC_ENTRY part, controlled by an integration archetype designed to mimic the incoming structure (such as an HL7v2 lab message) as closely as possible; FEEDER_AUDIT structures are used to contain integration meta-data. The result of this step is data that are expressed in the openEHR type system (i.e. as instances of the openEHR reference model), and are immediately amenable to processing with normal openEHR software.

In the second step, semantic transformation is effected, by the use of mappings between integration and designed archetypes. Such mappings are created by archetype authors using tools. The mapping rules are the key to defining structural transformations, use of terminological codes, and other changes. Serious challenges of course remain in the business of integrating heterogeneous systems; some of these are dealt with in the Common IM document sections on Feeder systems.

These standards define high-level requirements or compliance criteria which can be used to provide a means of normative comparison of openEHR with other related specifications or systems:

The following standards have influenced the design of the openEHR specifications:

The following standards are mainly domain-level models of clinical practice or concepts, and are being used to design openEHR archetypes and templates.

The following standards are used or referenced at a fine-grained level in openEHR:

The following standards are in use and require data conversion for use with openEHR:

The following standards are used or referenced in openEHR:

ITSs are created by the application of transformation rules from the "full-strength" semantics of the abstract models to equivalents in a particular technology. Transformation rules usually include mappings of:

ITSs are being developed for a number of major implementation technologies, as summarised below. Implementors should always look for an ITS for the technology in question before proceeding. If none exists, it will need to be defined. A methodology to do this is being developed. The figure below illustrates the implementation technology specification space. Each specification documents the mapping from the standard object-oriented semantics used in the openEHR abstract models, and also provides an expression of each of the abstract models in the ITS formalism.