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Journal of Biomedical Informatics 44 (2011) 869–880
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Journal of Biomedical Informatics
journal homepage: www.elsevier .com/locate /y jb in
Clinical data interoperability based on archetype transformation
Catalina Martínez Costa, Marcos Menárguez-Tortosa, Jesualdo Tomás Fernández-Breis ⇑Facultad de Informática, Universidad de Murcia, CP 30100 Murcia, Spain
a r t i c l e i n f o a b s t r a c t
Article history:Received 12 January 2011Accepted 17 May 2011Available online 27 May 2011
Keywords:Electronic healthcare recordsSemantic interoperabilityISO 13606OpenEHRArchetypesOntology
1532-0464/$ - see front matter � 2011 Elsevier Inc. Adoi:10.1016/j.jbi.2011.05.006
⇑ Corresponding author. Fax: +34 868884151.E-mail addresses: [email protected] (C. Mar
(M. Menárguez-Tortosa), [email protected] (J.T. Fernán
The semantic interoperability between health information systems is a major challenge to improve thequality of clinical practice and patient safety. In recent years many projects have faced this problemand provided solutions based on specific standards and technologies in order to satisfy the needs of a par-ticular scenario. Most of such solutions cannot be easily adapted to new scenarios, thus more global solu-tions are needed.
In this work, we have focused on the semantic interoperability of electronic healthcare records stan-dards based on the dual model architecture and we have developed a solution that has been appliedto ISO 13606 and openEHR. The technological infrastructure combines reference models, archetypesand ontologies, with the support of Model-driven Engineering techniques. For this purpose, the interop-erability infrastructure developed in previous work by our group has been reused and extended to coverthe requirements of data transformation.
� 2011 Elsevier Inc. All rights reserved.
1. Introduction
The present world is becoming more and more global. Citizensmove nowadays more often from one country to another for workor leisure. In addition to this, the technological development hasremoved many barriers from the past and citizens can use mostof their services from anywhere. As stated in [1], access to health-care services during temporary stays abroad is facilitated by theEuropean Health Insurance Card, although cross-border healthcareservices are not only limited but also account for only 1% of publichealthcare expenditure in the European Union. As a matter of fact,the clinical information of a patient can usually be available only atthe healthcare institution in which that piece of information wasgenerated. Hence, the semantic interoperability of electronichealthcare records (EHR) systems is a major challenge in eHealth[2,3]. That should allow healthcare professionals to manage thecomplete EHR of citizens, independently from the institutions thatgenerated the clinical data. Recent recommendations of the Euro-pean Commission [3,4] have stated that such semantic interopera-bility is essential to improve the quality and safety of patient care,public health, clinical research, and health service management. Inthe present work, semantic interoperability of clinical informationis defined as the ability of information systems to exchange andunderstand clinical information independently of the system inwhich it was created.
ll rights reserved.
tínez Costa), [email protected]).
Standards for the representation and exchange of EHR play acrucial role in the achievement of semantic interoperability. In thiswork, we are interested in standards based on the dual modelarchitecture [5], because the previously mentioned internationalrecommendations consider them a promising solution forachieving semantic interoperability of clinical information andknowledge. Dual model architectures are based on the definitionof information and knowledge separately by defining two concep-tual levels by using the reference and archetype models. Theyprovide a way of specifying this clinical information by means ofarchetypes. Archetypes represent the minimal information unitthat clinical information systems can exchange, thus they are thebasic semantic interoperability unit [6]. Moreover, archetypes alsoinclude the context for interpreting clinical information. ISO 13606[7], openEHR [8] and HL7 CDA [9] are the most widespread dual-model standards.
Our research group has been working for the last years in thedevelopment of a semantic interoperability infrastructure for dualmodel EHR standards. As a result of our work, we have developedmethodologies for the ontological representation of archetypes[10] and for transforming archetypes between EHR standards[11]. In this article we propose a methodology for making the ex-change of clinical data between dual model-based systems basedon different standards possible. In our approach, data transforma-tion is based on archetype transformation, and we will be able toreuse some of our previous results. Thus, our data transformationapproach consists of two transformations: (1) archetype transfor-mation and (2) data transformation. Our methodology intends tobe generic enough to be applicable to any EHR standard based onthe dual model architecture. In this paper, the methodology will
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be applied to ISO 13606 and openEHR. The first one has gainedimportance in Europe in recent years and a sign of it can be consid-ered that in 2008 was selected the interoperability standard inSweden [12]. On the other hand, openEHR has a large communityof users and developers, and it is used in countries such as Austra-lia or Holland. As a result, there are many open-source tools andarchetypes available for it that are useful for validating themethodology.
Next, the structure of this work is described. First, Section 2 pro-vides some background about some of the existing interoperabilityapproaches and EHR standards. Then, in Section 3 the methodologyproposed is presented. This section will summarize our previouswork and describe the solution proposed for data transformation.Finally, some discussion and conclusions are put forward inSection 4.
2. Background
This section presents some of the recent EHR interoperabilityapproaches. Afterwards, a brief introduction to EHR standards,focusing on those following the dual model architecture is pro-vided. Finally, a description about clinical data and archetypes isgiven. This will include how both are represented and used in thiswork.
2.1. EHR interoperability approaches
In the last years many projects have pursued the interoperabil-ity of EHR information systems. The different approaches have pro-posed solutions based on specific standards and technologies inorder to satisfy the needs of a particular scenario, but no globalinteroperability frameworks have been provided so far, as we de-scribe next.
The first group of approaches uses XML technologies forachieving interoperability. An example is the interoperabilityexperience between a propietary system and HL7 CDA that hasbeen run in Taiwan [13]. In this solution, both representationmodels are represented in XML and their mappings are definedby using XSLT rules. The second group of approaches makes useof XML and OWL, although with different purposes. In [14], theinteroperability among different health care systems conformingto different EHR standards was pursued by annotating the WebService messages through archetypes defined in OWL. In such anapproach, mappings between these archetypes are defined manu-ally by using a graphical tool. Then, these mappings are applied toXML clinical data instances. The same research group presented in[15] an approach based on archetypes, ontologies and semantictechniques for the interoperability between HL7 CDA and ISO13606 systems. This work shares with their previous results thatarchetypes are represented in OWL, but they approach the problemby using HL7 RIM as basic and common information model.Consequently, this information model supports the semiautomaticdefinition of the mappings between the standards. The mappingsare defined between archetypes by comparing whether the struc-tures used in both standards are specializations of the same classof the HL7 RIM by using reasoning mechanisms. The resultingmappings are applied to XML clinical data, which are transformedinto OWL for the reasoning and then transformed back into XML.
Another real interoperability experience is the one betweenopenEHR and the Cambio COSMIC system [16]. In this work map-pings between both information models were defined. This solu-tion, like the one run in Taiwan, is oriented to solve the specificinteroperability problem of these two systems and therefore diffi-cult to adapt to its use with other clinical models or standards.
Given the importance of the semantic interoperability, the mainclinical standardization organizations, ISO, HL7 and openEHR, aremaking an effort for harmonizing their specifications. Evidence ofthis is part three of ISO 13606 named ‘‘Reference archetypes andterm lists’’ that provides an informative guide in order to representclinical information codified according to HL7 and openEHR byusing ISO 13606 structures. Moreover, the specification states that,in order to achieve full semantic interoperability of systems basedon these norms, it is necessary to harmonize the vocabulary andthe data types used. In order to harmonize data types, the ISO21090 standard is being developed. In the near future the currentEHR standards are probably going to support that norm. In orderto harmonize the vocabulary used, clinical terminologies will playan important role. On the other hand, the openEHR Foundation isdefining the mappings between ISO 13606 and openEHR datastructures, and between openEHR and ISO 21090 data types. Inaddition to this, Detailed Clinical Models (DCM) [17] have been re-cently defined as an initiative for defining clinical informationindependently of an specific clinical standard but with the aim ofoffering the possibility of being transformed into other standards.
In summary, each particular solution helps to provide access tothe patient clinical information for some clinical organizations.One of the disadvantages of these approaches is that they do notpropose frameworks and methods easy to apply to different stan-dards. On the one hand, XML technologies provide a limitedsemantic model which does not seem the best solution forachieving the semantic interoperability objective since, for in-stance, the integration of clinical data with terminologies wouldbe difficult. In particular, the usage of XSLT for defining the map-ping rules is no longer the best available option given the availabil-ity of mapping and transformation languages created by theModel-driven Engineering community which have better proper-ties in terms of maintenance, reusability and support to softwaredevelopment processes. On the other hand, the use of OWL andontologies for supporting such interoperability processes is a goodpractice that is recommended by the Semantic Health report [3], soglobal solutions should make use of such technology.
2.2. EHR standards
The digitization of clinical records has been a major researchissue for the last decades. An increasing number of countrieshave been making efforts in the digitalization of clinical recordssince the GEHR project [18] began the design of an electronichealth architecture in the late 80s. As a consequence of those ef-forts, several EHR standards were proposed, including HL7, ope-nEHR and ISO 13606. The last two mentioned standards follow adual model architecture, which is based on the definition of twodifferent conceptual levels, namely, information and knowledge.Each level uses a different data model, thus, the information le-vel uses the reference model and the knowledge level uses thearchetype one. The reference model defines the set of entitiesthat form the building blocks of the electronic healthcare record.The archetype model is used to define clinical concepts in theform of structured and constrained combinations of the entitiescontained in the reference model. These clinical concepts arenamed archetypes and are usually defined using the ArchetypeDefinition Language (ADL) [19]. This language provides an ab-stract syntax which can be used to express archetypes for anyreference model in a standard way.
Therefore, both ISO 13606 and openEHR share the samearchetype model but have different reference models. OpenEHRdefines richer data structures and types than ISO 13606. Thus,openEHR allows defining clinical information with more detail.An extract of both reference models is shown in Fig. 1. Bothmodels consist of a set of concepts that organize the EHR
Fig. 1. OpenEHR and ISO 13606 main data structures.
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information hierarchically. In openEHR and ISO 13606 the clini-cal information of a patient is usually defined by using a COMPO-SITION concept, which is similar to a clinical form, and severalCOMPOSITION elements can be grouped in a FOLDER. Each formgroups the information in SECTION entities and in each one, clin-ical concepts can be defined by using ENTRY concepts. In ISO13606 there is one ENTRY type, but, in openEHR, there are sometypes of ENTRY according to the information they contain,namely, ADMIN_ENTRY, OBSERVATION, INSTRUCTION, EVALUATIONor ACTION. In addition to this, openEHR also defines a GENER-IC_ENTRY entity to facilitate interoperability with other standardssuch as ISO 13606. The information contained in an ENTRY is or-ganized by using a table, list or tree by means of a CLUSTER inISO 13606 or specific data structures such as ITEM_TABLE, ITEM_-TREE, ITEM_LIST or ITEM_SINGLE in openEHR. Finally, the leafnode of the EHR hierarchy is the ELEMENT that will contain adata value.
2.3. Archetypes
As has been mentioned, archetypes can be used to define clini-cal concepts such as heart rate, a laboratory test, and a blood pres-sure measurement. An archetype can be defined as a specializationof another one, can include another archetype fragment in it, canbe used in combination with others by means of templates, andso on. They constitute a standardized way of capturing clinical data
according to the archetype model, which provides the context forinterpreting the clinical information.
These are usually defined in ADL. An ADL archetype has the fol-lowing four main sections: header, description, definition andontology. The first two ones give information about the name ofthe archetype, the language it is written in, original author, pur-pose, etc. The definition section is the most important one becauseit contains the structures and constraints associated with the clin-ical concept defined by the archetype. The ontology section pro-vides the textual description for each concept from the definitionsection and bindings to other terminologies.
Fig. 2 shows an excerpt of the definition part of the ISO 13606ADL archetype for defining regular drugs. The root node of thisarchetype is the composition (COMPOSITION[at0000]) which com-prises the ENTRY[at0001]. This ENTRY contains the followingELEMENTs:
� The name (ELEMENT[at0008]), route (ELEMENT[at0006]) and sideeffects (ELEMENT[at0002]) of the drug are defined by using a texttype (SIMPLE_TEXT[at0018], SIMPLE_TEXT[at0016] andSIMPLE_TEXT [at0014]). The name and side effects are free text,but the route is defined by a list of options (Auricular, Buccal,Oral, etc .).� The period of time (ELEMENT[at0009]) in which the drug is
taken, is defined by means of a boolean type (BL[at0019]) thatsays whether it is during a long term.
Fig. 2. Excerpt of the ISO 13606 regular drugs archetype.
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� The dosage of the drug (ELEMENT[at0003]) by using a physicalquantity type (PQ[at0024]) that constrains the unit of measureto (mg) and the value to be a positive quantity.
2.4. Data extracts
In dual model-based EHR standards, data is usually representedin XML according to the schemas defined for each standardalthough, to date, some standards do not have an official one. Inthis work, we will use the non-official XML Schema defined forISO 13606 that is publicly available at [20], and the official onefor openEHR [8].
Fig. 3 shows an example of data extract for the ISO 13606regular drugs archetype. This extract contains the definition ofthe name of the drug (Ranitidine), its dosage (75 mg) and its route(Oral). Moreover, it also mentions that the data has been capturedusing the regular drugs archetype. The complete extract can befound in [21].
3. Method
In this section the method for transforming clinical data in-stances between dual model systems is presented. This methodreuses some pieces developed by us in previous works, whichwill be briefly described in this section because they are funda-mental for understanding the method for data transformation.
Then, the implementation and validation of the method will bereported.
3.1. Overview
The method proposed here intends to enable systems based ondifferent dual model standards to exchange clinical data. Our sce-nario comprises two EHR systems, namely, A and B, that have beenused for capturing the EHR of a particular citizen, and A needs toretrieve some information stored in B. In an ideal situation, bothsystems would share a representation standard and semanticinteroperability would be granted, but this situation is unrealisticnowadays. If we assume that both systems use different standardsbased on the dual-model architecture, a solution for the achieve-ment of the semantic interoperability could be the one shown inFig. 4. There, two different steps are identified:
� Archetype transformation: the archetypes used in B for capturingthe data to be exchanged are transformed into archetypes for A.� Data transformation: the data captured in B are transformed into
data for A.
Our solution for the interoperability of data will provide methodsfor both archetype and data transformation. For this purpose, twomajor previous results of our group will be reused: (1) a methodfor obtaining the ontological representation of archetypes [10];and (2) a method for transforming archetypes between ISO 13606
Fig. 3. ISO 13606 regular drugs XML data extract.
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and openEHR standards [11]. Hence, this work constitutes our thirdstep towards the achievement of the semantic interoperability be-tween ISO 13606 and openEHR.
3.2. Ontology infrastructure
In dual model architectures, the reference model, the archetypemodel and terminological definitions are each other related byusing string properties, which is not an optimal decision for pursu-ing semantic goals. In order to implement the interoperability ap-proach above presented it would be better to provide a moreformal and precise definition of those relations. For this purpose,
we designed an ontology infrastructure for the support of interop-erability [22], which is shown in Fig. 5 for ISO 13606 and openEHR.
This infrastructure comprises a series of ontologies that werethe result of the semantic interpretation of both reference andarchetype models of the standards. Both ISO 13606 and openEHRontologies combine concepts from both reference and archetypemodels. They reuse the specific ontologies developed for the refer-ence models (ISO 13606-RM and openEHR-RM) and the archetypemodel (common to both standards). The archetype structure is ex-pressed in these ontologies in a more processing-friendly and com-prehensible way. For instance, concepts as archetype, archetypedescription, archetype description item, occurrences, clinical data-type or archetype term exist in the archetype model, but more
Fig. 4. Data Interoperability between two dual model-based EHR systems.
Fig. 5. Ontologies relations map.
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standard-specific ones such as folder, composition, section, entryor element belong to the reference model.
In order to define the transformations between both standards,the definition of these ontologies might be enough. However, weaim to develop a generic and extensible architecture, capable ofdealing in the future with other standards such as HL7 CDA. Thisself-imposed requirement led us to develop a common ontologyfor EHR standards. This ontology covers the global aspects of arche-types in dual model approaches and offers a common representa-tion for them. This collection of ontologies can be retrieved from[23].
3.3. Archetype transformation
The archetype transformation can be considered an initial steptowards the transformation of data instances. In [11] we pre-sented an approach for transforming archetypes between ISO13606 and openEHR standards. There, both reference modelswere analyzed by identifying their common and disjoint entities.As a result, a set of conceptual mappings at concept and propertylevel for defining how a particular component of an archetype canbe transformed into a component of the other standard wasprovided [24].
Fig. 6. Archetypes transformation architecture.
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As it has been mentioned in Section 2.2 both standards use thesame archetype model but different reference models. Every ISO13606 entity has a similar one in openEHR, but the opposite doesnot happen because openEHR provides richer data structures anddata types. For instance, the openEHR ENTRY is an abstract conceptwith a number of specializations but the ISO 13606 ENTRY is a con-crete entity without specializations. Thus, all openEHR ENTRYs haveto be transformed into the same concept in ISO 13606. This transfor-mation requires adapting their internal structure by means ofCLUSTER and ELEMENT entities in ISO 13606. Moreover, theopenEHR reference model contains more entities than ISO 13606.For instance, the openEHR ITEM_STRUCTURE hierarchy has no directcorrespondence in ISO 13606. Consequently, these entities are alsorepresented in ISO 13606 as a CLUSTER/ELEMENT composition.
Therefore, mappings from openEHR to ISO 13606 might beinterpreted as a generalization. In order to keep the meaning ofthe clinical data, each ISO 13606 data structure preserves the nameof the openEHR property and type in the meaning property, whichis defined for every ISO 13606 data structure. This informationcould be used in the target system to interpret more accuratelythe transformed archetype.
Otherwise, every ISO 13606 concept has a corresponding one inopenEHR, although their properties might also differ. In this case,the property can be mapped into a CLUSTER or ELEMENT, and thename of the ISO 13606 property is preserved in the uid propertyof openEHR data structures. This attribute has been used becauseit is not currently exploted in this standard.
With regard to data types, each standard defines its own set.The number of data types in ISO 13606 is lower than in openEHR,and some common ones are defined differently. Moreover, a CLUS-TER/ELEMENT composition plus using the meaning property is notapplicable for data types. Therefore, lossless mappings cannot begranted for some of them. For instance, the property codingScheme-Name of the ISO 13606 data type physical quantity (PQ) does nothave a similar property in the openEHR DV_QUANTITY type. Thus,this information could not be transformed. According to our expe-rience, nearly all situations in which some information cannot betransformed is related to some particular data types. However,those spurious losses do not prevent from getting the archetypestransformed. Moreover, the method manages and reports suchlosses.
The architecture proposed for transforming ISO 13606 arche-types into openEHR ones is shown in Fig. 6. There, two layers canbe distinguished: ontology and metamodel.
The ontology layer provides the formal semantics of our do-main, and is composed of the ontologies previously described.The metamodel layer contains the metamodels corresponding tothe archetype model and to the representation defined in the
ontology layer, that is metamodels for the ISO 13606, openEHRand Common ontologies. The transformation mappings areformalized and the transformation of archetypes is done in themetamodel layer due to the availability and maturity of tools pro-vided by Model-driven Engineering. The conceptual mappings aredefined between the particular standard, ISO 13606 or openEHR,and the Common metamodel at concept and property levels andare implemented by using a model transformation language. Thearchetype transformation method requires these four phases:
1. The ISO 13606 ADL archetype is transformed into a modelaccording to the ontological representation of ISO 13606.
2. The ontological ISO 13606 model is transformed into a modelconforming to the Common representation.
3. The Common model is transformed into a model conforming tothe ontological representation of openEHR.
4. The openEHR model is transformed into ADL.
The process from openEHR to ISO 13606 would be similar. Ifthis transformation method is applied to the ISO 13606 regulardrugs archetype presented in Section 2.3 the ISO 13606 COMPOSI-TION will be transformed into an openEHR COMPOSITION concept.In ISO 13606, it organizes its content by using an ENTRY but inopenEHR an ENTRY is an abstract concept that is specialized inmore specific ones such as OBSERVATION, ACTION, andINSTRUCTION. Here, it will be represented as a GENERIC_ENTRY.The previous ISO 13606 ELEMENTs will be now openEHR ones thatwill be grouped by a tree structure (ITEM_TREE) and each ISO13606 data type will be transformed into its correspondingopenEHR representation.
3.4. Data transformation
In this section we describe the new developments of the frame-work for transforming data between dual-model EHR standards.First, we had to extend the archetype transformation method. Thismethod is able now to manage and use the trace of the transforma-tion process. As it can be observed in Fig. 6, the transformation ofarchetypes is defined between the specific standards and the com-mon representation. This transformation process creates two tracemodels for each archetype transformation, which contain thetransformations that have been performed in the process.
Both trace models can be now processed for obtaining the cor-responding semantic trace. A semantic trace contains semanticmappings, which associate the concepts and properties of thesource and target archetype. They are named semantic becausethey are obtained from the ontological representation of thearchetype. Semantic mappings must be distinguished from the
Fig. 7. The semantic mapping for units.
Fig. 8. ISO 13606 and openEHR data transformation schema.
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conceptual ones, which are the transformations defined at refer-ence model level, whereas the semantic mappings are the transfor-mations that have been executed for a particular archetype. Eachsemantic mapping has two semantic paths represented as a listof [concept]property/ elements, which provide the route of con-cepts and properties of the ontological representation of the arche-type. Fig. 7 depicts the semantic mappings obtained for the unitsproperty of the dosage from the regular drugs example.
However, in order to transform clinical data instances, semanticmappings are not enough. They provide the conceptual mappingsestablished between the ontological representation of archetypesbut clinical data are defined as XML extracts according to ADL arche-types. Both ADL archetypes and data extracts are organized in a treestructure, so their elements can be accessed by using a path whichaccounts for the nodes from the root of the tree to the desiredone. The path for accessing the archetype nodes is called an ADLpath [25]. It is formed from an alternation of segments made up ofan attribute name and an optional object node identifier predicatedelimited by brackets and separated by slash characters. TheADL path for the value of units in the ISO 13606 regular drugsarchetype example is /content[at0001]/items[at0003]/value[at0024]/
units[at0025]/codeValue. It is built by the nodes from the root of thearchetype to the codeValue property of units. The correspondingpath in a sample data extract would be /extract[id]/data/content[at0001]/items[at0003]/value[at0024]/units[at0025]/code-Value. It is the same but adding the extract header. In this work,these paths will be called syntactic paths because they are obtainedfrom the XML representation of the extracts or the archetypes.
Thus, in order to allow the transformation of data extracts, thealignment of each piece of data is needed, that is, the mappingfrom the source extract to the target extract. Each piece of datais in this case identified by a syntactic path. Such mappings at datalevel are named syntactic mappings and are obtained from thesemantic mappings.
The general process for data transformation consists of twosteps, namely, generation of the syntactic mappings and transfor-mation of the data. This process for ISO 13606 and openEHR is de-picted in Fig. 8. There, it can be observed how the semantic traceobtained from the archetype transformation permits obtainingthe syntactic mappings established between the ISO 13606 andopenEHR ADL archetype representations, that is, the pairs of sourceand target syntactic paths. In the figure, an ISO 13606 data extract
Fig. 9. OpenEHR regular drugs XML data extract.
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has to be transformed into openEHR. Thus, the source syntacticpaths will be the ones obtained from the ADL ISO 13606 archetyperepresentation, and the target syntactic ones will be those corre-sponding to the ADL openEHR archetype. Once all the syntacticmappings have been obtained, each source syntactic path will per-mit accessing data in the ISO 13606 extract, and each target syn-tactic one will provide the path for defining such data accordingto the openEHR representation.
Therefore, in order to transform the ISO 13606 regular drugs pa-tient data presented in Section 2.4 to their openEHR representa-tion, two main tasks are needed: (1) transformation of the ISO13606 regular drugs archetype into its representation accordingto the openEHR standard; and (2) transformation of the ISO13606 regular drugs data extract in an openEHR extract.
The first task has already been explained in Section 3.3. As a re-sult of this task, the semantic trace between the concepts andproperties of the regular drugs archetype for both standards is ob-tained. In order to carry out the second task, the semantic trace isused for generating the syntactic mappings. These last ones willprovide the route of each data unit in the source data extract,namely, source syntactic path, and the corresponding one in thetarget data extract, namely, target syntactic path.
The transformation of the ISO 13606 extract generates the re-sult published at Ref. [21], and whose excerpt is shown in Fig. 9.There, it can be observed how the different data structures andtypes have been transformed into the corresponding openEHR oneslike GENERIC_ENTRY, DV_TEXT, or DV_QUANTITY, etc. The structureof the content section of this transformed extract is described next.The type of data is COMPOSITION. Its content is a GENERIC_ENTRY,whose id is at0001. The data associated with this entry is an ITEM_-
TREE, whose items include an ELEMENT. The name of this elementhas the value Dosage, and the value of the element is a DV_QUAN-TITY, whose id is at0024. An openEHR DV_QUANTITY has the prop-erties magnitude and units, whose values are, respectively, 75 andmg. Such values have been obtained by applying the correspondingsemantic and syntactic mappings.
3.5. Validation
This transformation method has been validated by using Posea-cle Converter [26]. This tool provides functionality for both thetransformation of an ADL archetype into OWL [27] and the transfor-mation of ISO 13606 archetypes into openEHR ones and vice versa.The Poseacle Converter combines different technologies and lan-guages included in our approach: Model-driven Engineering,Semantic Web and Java technologies. Apart from the online accessto the tool a RESTful service for doing the transformation and anAPI to invoke it have been developed.
As a result of the current research work, an option for datatransformation has been added to this tool. The option has beenadded as the tab Data Transformation (see Fig. 10) whose interfaceis very similar to the one for archetype transformation. In order toget an extract transformed, the user has to perform the followingactions:
� Input the ADL archetype by browsing the local file system orentering its URL.� Input the XML data extract by browsing the local file system or
entering its URL.� Select the source and target EHR standard (ISO 13606/
openEHR).
We have performed a technical validation of the process. Thismeans that we have executed a series of data transformation pro-cesses and the results have been analyzed. The first set of testswere focused on the correct execution of the transformation rules.For this purpose, unit tests were executed and we checked the cor-rectness by comparing the expected and the actual results. Someextracts and archetypes were designed for this process, containingmost of the data types and clinical structures of both standards.
Next, we tested the correct transformation of data extracts. Forit, we manually created some sample extracts and we used someextracts facilitated by some colleagues. Those extracts were trans-formed both manually and by the Poseacle Converter and the cor-rectness of the results was checked. The publicly availablecorresponding data extracts can be accessed in [21]. The results de-rived from the data transformation are in line with the ones de-rived from the archetype transformation. The data that were nottransformed correspond to properties or concepts that were nottransformed at archetype level.
Finally, we analyzed the time performance of the process byusing the Poseacle Converter. In order to evaluate this perfor-mance, it should be noted that the server has a 2.27 GHz Core 2Duo processor and 4 GB of memory, and that this server is not fullydedicated to this tool. The process includes transformations at botharchetype and data level. Consequently, both times will be consid-ered independently. Our results show that, as expected, the timespent in the transformation of the archetypes and data are directlyrelated to the size of the archetype and the extract. In general, thetransformation at archetype level takes longer than the ones atdata level, although both times are, in our opinion, satisfactory.The time spent in the archetype transformation ranged, in ourexperiments, between 2 and 7 s, whereas data transformationstook between 1 and 2 s. This would make the complete processto take between 3 and 9 s. It should be pointed out that, in thecontext of a real deployment of the system, a library of
Fig. 10. Poseacle Converter: ISO 13606/openEHR data transformation.
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transformations would avoid the need for the transformation atarchetype level in most cases, so the global transformation timewould be little more than the time of the transformation at datalevel.
4. Discussion and conclusions
The achievement of semantic interoperability of EHR systems isconsidered essential to improve the quality and safety of patientcare, public health, clinical research, and health service manage-ment. The dual model architecture was developed with the aimof facilitating the semantic interoperability of EHR informationsystems. Unfortunately, a limited number of EHR systems makeuse of such standards for representing the clinical informationand this makes the achievement of semantic interoperabilityharder.
In dual model architectures, archetypes are considered the basicinteroperability unit. Nowadays, there are several standards basedon this architecture. In order to enable the information and knowl-edge sharing between them, transformation methods have beendeveloped by our research group in recent years. Hence, this workaims to offer mechanisms that help the adoption of dual modelarchitectures, by providing mechanisms for the exchange of clini-cal information between different standards. More concretely, wehave addressed in this work the transformation of archetyped-databetween ISO 13606 and openEHR.
The present work reuses some of our previous results, such asthe ontological infrastructure [28], the ADL to OWL transformationtool [10] and the archetype transformation method [11]. They werefocused on archetypes and their transformation between standardsand have provided us the infrastructure for carrying out the workwe present here, the transformation of clinical data instances. Thisinfrastructure will be also the technological support for our futureresearch and developments. The archetype transformation hasbeen integrated into the LinkEHR [29] editor and has also been
used in some tools developed in the research group which has con-tributed to a better technical validation of the different modulesand tools developed. Since the beginning of the development ofthe methodology, the fact that it was based on previously devel-oped methods and tools has allowed us to make them someimprovements or extensions in order to facilitate the achievementof the next goals. For instance, among the improvements, the traceof the archetype transformation can be obtained as a result of thiswork. Apart from being useful for data transformation, it has al-lowed us to provide the user with an informative report of the pro-cess, that is, what has been transformed and what has not. Thegood results and the efficiency of the process makes us believe thatour design decisions have been adequate in the last years.
The backbone of our technological framework combines ontol-ogies and metamodels. Both technological worlds are commonlyaccepted for the pursuit of semantic interoperability, even withoutbeing combined. The crucial role played by ontologies in interoper-ability settings is described in [30], and the recent effort presentedin [31] uses a common ontology for facilitating the interoperabilitybetween ECG data standards. On the other hand, models drive thesemantic services oriented architecture used in [32]. Hence, ourtechnological infrastructure is based on the ontological representa-tion of archetypes that will allow us to use in the future externalterminological resources in order to improve the archetype anddata transformation. Moreover, the representation of archetypesas ontological models has provided us with an interesting toolfor a more natural access to the archetype information, in whichfrom each concept it is possible to access to all its related informa-tion: occurrences, cardinality, definition, bindings, etc.
In our approach, the transformation of data instances consists oftwo steps: (1) archetype transformation and (2) data transforma-tion. The transformation of archetypes is executed as describedin [11]. This process is based on the conceptual mappings betweenthe reference models of ISO 13606 and openEHR defined by us, sothey do not correspond to any community consensus. In fact, the
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openEHR Foundation is defining its own mappings [33], which aresimilar to ours. The archetype transformation process had to be ex-tended to deal with the needs of the data transformation step.Thus, the new archetype transformation process generates thesemantic trace which accounts for the set of transformations ap-plied to the source archetype in order to get the target one. Sucheffective transformations are called semantic mappings; they con-sist of the paths of the archetype concept or property in the sourceand target ontological archetype.
The method proposed for transforming archetyped data in-stances makes use of that semantic trace and those semantic map-pings to obtain the syntactic mappings. Each syntactic mappingincludes two syntactic paths. Each syntactic path stands for thepath of a particular archetype concept or property in the syntacticADL archetype. The syntactic mappings are used for accessing thesource clinical data and defining the target data extract in the tar-get standard representation. The transformation of data instancesmight have been done by using XSLT transformations and, in suchsolutions, the semantic and syntactic mappings would not be re-quired. However, as it has been previously mentioned, XML tech-nologies have a limited semantics which make them not optimalfor the semantic interoperability challenge. This does not meanthat such technologies should not be used, since they are currentlyused by most standards for the representation of data extracts, butthey should not constitute the technological kernel for definingand executing the mappings between the EHR standards. TheModel-Driven Engineering community has developed languagesfor defining and executing such mapping. Besides, such advancedSoftware Engineering approach also facilitates the combination ofdifferent technological spaces such as ontologies, archetypes anddata extracts. Thus, a solution based on such mapping andtransformation languages facilitate the development, reusability,extensibility and maintenance of our solution.
The transformation of data instances is not a lossless process. Asdiscussed in [11], the transformation of archetypes may sufferfrom some loss of information, although this just happens in a re-duced number of situations. Most information losses occurs in thedata types transformation and the ISO 21090 standard is being de-fined in order to provide a set of harmonized data types to be usedin information exchanges. Both ISO 13606 and openEHR will likelyuse this new standard in the near future. Thus, our implementationwill be also adapted to support it. In the situations in which somedata cannot be transformed, the Poseacle Converter not only showsthe data extract, but the user may also ask for the report of thetransformation process, which includes the semantic trace of theprocess, and the data that has not been transformed. Consequently,the user is informed of what has been transformed and what hasnot, and may take the appropriate decisions and actions. In addi-tion to this, the usage and processing of the meaning property al-lows the method to be reversible, although this has not beenimplemented yet in our conversion tool.
To the best of our knowledge, this is the first implementation ofdata interoperability between these standards, although differentapproaches for semantic interoperability have been proposed in re-cent years. Most of them are designed to give a solution to a veryspecific problem. In this way, mappings are usually defined directlybetween the EHR format representations or standards withoutusing any common representation that would enable a moregeneric solution. Therefore, the resulting solutions are verydifficult to maintain and very little adaptable. None of theseapproaches, including ours, formalizes the mappings between thedifferent standards in an easy-to-reuse manner. Most approachescode the mappings into the software or, as we do, in a transforma-tion language. Therefore, this makes it difficult for other research-ers to reuse currently available solutions. Recently, the Ontology
Management Working Group proposed a standard for OntologyMapping [34], although it has not been adopted by the community.In addition to this, there are many mapping languages in Model-driven Engineering. However, the community has not developeda language good enough for defining the mappings between EHRstandards.
Our solution is not perfect, since we have not used so far the ter-minological knowledge contained in the archetypes for optimizingthe transformation processes. Thus, further research will be con-ducted for using SNOMED-CT as part of the transformation process.In fact, this offers a discussion about to what extent our solution issemantic. Our current solution incorporates a series of semanticcomponents, although the transformation process is not semanticin a strict sense. The root of the approach is a set of ontologiesfor EHR standards written in OWL, which is a language whose con-structs have a formally defined meaning. Thus, archetypes can beexpressed in a formal language such as OWL, and any semanticactivity that could not be efficiently done with ADL can be per-formed on them. Hence, once archetypes are expressed in OWL,we can do semantic tasks such as the ones included in our Arche-type Management System [35]. This system allows for makingautomatic classification and search of archetypes by annotatingthem with external resources represented in OWL such as termi-nologies or other domain ontologies. It would be harder to performsuch tasks with ADL archetypes. On the other hand, the mappingsbetween the EHR standards are defined by using their ontologicalrepresentations, and relations are defined between them, with for-mally defined meaning. This is therefore another sign of use ofsemantics in our approach. However, such semantics is not fullyexploited in the archetype transformation process.
Our ontologies define the structural semantics of the archetypesand reference models, but terminological knowledge is not seam-lessly integrated. Thus, terminology can be associated with theterms included in the ontological archetypes, although as informa-tion units rather than knowledge units. This means that the termi-nological knowledge is not natively represented as part of theontological knowledge, so additional processing would be requiredto combine the structural and terminological knowledge for sup-porting the transformation process. This does not prevent our dataand archetype transformation process from obtaining correct re-sults but, from a semantic point of view, the results could be moreprecise.
In summary, we have developed methods for transforming datainstances between two important EHR standards, namely, ISO13606 and openEHR. The research results obtained in the last yearshave produced an interesting solution for the semantic interoper-ability of these standards, because we have developed automaticmethods for the ontological representation of archetypes and forthe transformation of both archetypes and data instances betweenISO 13606 and openEHR. Our results show that such exchange andsharing is possible, and we think that our approach could be ap-plied to other dual model standards. Moreover, standards using asimilar technological approach, like HL7 CDA, could also be inte-grated in our framework. For this purpose, we would need to buildthe ontology for this standard and to define the mappings with thecommon ontology. The addition of a new standard might make theextension of the common ontology necessary. These new entitiescould also help to enhance the previously defined mappings.
Finally, it should be pointed that, although the dual modelarchitecture is mainly used for the interoperability of EHR informa-tion systems, it might be used in other domains for which an infor-mation model can be defined and the domain concepts can bedefined by constraining the entities of that information model. Inthat case, our methodological approach could be applied for thesemantic interoperability of standards for such domains, for
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instance, for exchanging bank or billing records. In this case, theontological infrastructure and the mappings should be definedfor the corresponding standards.
Acknowledgments
This work has been possible thanks to the Spanish Ministry forScience and Education through Grants TSI2007-66575-C02-02 andTIN2010-21388-C02-02. Special thanks to Daniel Karlsson, MarceloRodrigues dos Santos and Diego Boscá for providing us with dataextracts for the evaluation of our method.
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