37-Semantic Integration in Healthcare Networks

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Semantic integration in healthcare networks

Richard Lenz a , , Mario Beyer a , Klaus A Kuhn b

a Philipps-Universit ¨ at Marburg, Institut f ¨ ur Medizinische Informatik, Bunsenstrasse 3, 35037 Marburg, Germanyb Technische Universit ¨ at M ¨ unchen, Lehrstuhl f ¨ ur Medizinische Informatik, Munich, Germany

a r t i c l e i n f o

Article history:Received 31 December 2005Received in revised form23 March 2006Accepted 2 May 2006

Keywords:Information systems [MeSH]Information management [MeSH]Hospital information systems[MeSH]

a b s t r a c t

A seamless support of information ow for increasingly distributed healthcare processes

requires to integrate heterogeneous IT systems into a comprehensive distributed infor-mation system. Different standards contribute to ease this integration. In a researchproject focussing on the development of a reference architecture for inter-institutionalhealth information systems, we identied concurring standards currently in use. Wetherefore categorized these integration standards by distinguishing between technicaland semantic integration on the one hand, and data and functional integration onthe other hand. In addition, standards for semantic integration are roughly categorizedaccording to their scope. By placing standards into a corresponding matrix a “seman-tic gap” is revealed, which cannot be covered by standards as it contains volatilemedical concepts. As a conclusion, it is recommended to conceptually consider thenecessity of system evolution in system architectures and also in future integrationstandards.

© 2006 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

Healthcare increasingly changes from isolated treatmentepisodes towards a continuous treatment process involvingmultiple healthcare professionals and various institutions.This change motivates comprehensive, inter-institutional ITsupport in health information systems and imposes newdemanding requirements for IT [1]. IT applications shouldguide data acquisition in a way that data are placed in ameaningful context from thebeginning,so that they arereadyfor reuse in different contexts without the need to manu-ally index or transform the data. To achieve such an IT sup-port, heterogeneous IT systems have to be integrated into acomprehensive distributed information system. Integratingautonomous software components, however,is a difcult task,as individual applications usually are not designed to cooper-ate. Applications are often based on differing conceptualiza-tions of the application domain. Today powerful integration

Corresponding author . Tel.: +49 6421 28 66205; fax: +49 6421 28 63599.E-mail address: [email protected] (R. Lenz).

tools (e.g. application servers, object brokers, different kindsof message-oriented middleware, and workow managementsystems [2]) are available to overcome technical and syntac-tical heterogeneity of autonomous system components. Yet,semantic heterogeneity remains as a major barrier to seamlessintegration of autonomously developed software components(cf. [3]). Semanticheterogeneity occurs whenthere is disagree-ment about themeaning, interpretationor intendeduse of thesame or related data [4]. It occurs in different contexts, likedatabase schema integration, ontology mapping, or integra-tion of different terminologies. The underlying problems aremore or less the same, though they are often complex andstill poorly understood. Stonebraker characterizes disparatesystems as “islands of information” and points out two majorfactors which aggravate systems integration [5]:

1. Each island (i.e. application) will have its own meaning of enterprise objects.

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2. Each island will have data that overlaps data in otherislands. This partial redundancy generates a serious dataintegrity problem.

Based on this statement, data integration can be led backto a mapping problem (how to map different conceptual-izations in a semantically correct way) and a synchroniza-

tion problem (how to ensure mutual consistency of redun-dant data which are stored in different databases under thecontrol of autonomous applications). The mapping problemis essentially related to the schema integration problem of database systems, which has been extensively discussed inthe database literature in recent years (e.g. [6–9]). A major per-ception in data integration research has been that schemaintegration cannot be automated in general. In [10] it is stated:“The general problem of schema integration is undecidable .” Heilerstates that “ understanding data and software can never be fullyautomated ” [11]. As a consequence, theprocess of schema inte-gration always needs a human integrator for certain semanticdecisions. Colomb even goes a step further by stating thatthere are cases where no consistent interpretation of hetero-geneous sources is possible ( “fundamental semantic heterogene-ity”) [12]. In such cases one either has to accept a low degreeof data quality, or systems have to be modied to resolve fun-damental semantic heterogeneity.

In order to reduce the integration efforts caused bysemantic heterogeneity standards for systems integration areneeded. Moreover, as medicine is a rapidly evolving domain,concepts for system evolution are needed. Fortunately, thereare already far reaching standards that support informationinterchange in the medical domain. Yet, healthcare softwareis still far away from plug and play compatibility, and systemsintegrationis typically a difcult process. In a researchprojectin which we focus on the development of a reference archi-

tecture for comprehensive information systems in healthcarenetworks [1,13], we have identied concurring and semanti-cally overlapping standards. To get an overview of the stan-dards’ characteristics and interrelations, we have arrangedthem to a system of standards which we nd to be helpfulfor architecture development.

2. Objectives

In this article we try to clarify how different standards con-tribute to systems integration by distinguishing differentaspects and dimensions of integration. The objective of this

approach is to identify and characterize the “semantic gap”which is not covered by current standards, and which isresponsible for the high effort for systems integration. Thegoal of this clarication is to derive recommendations forfuture system architectures and standards development.

3. Methods

At a conceptual level, information systems are designedaround three layers: presentation, application logic, andresource management [2]. According to this well knownabstract model of information systems, we distinguished dif-

ferent aspects of integration:data integration, functional inte-gration and presentation integration:

• Data integration: we have already characterized semanticheterogeneity as themaincause forhighintegration efforts.We thereby focused on data integration. The reason for thisis that data integration is considered to be the most impor-

tant precondition for further integration. It is the backboneand starting point of each successful integration project,because any process control always requires a meaningfulexchange of data, too. The goal of data integration is to cre-ate a unique semantic reference for commonly used dataand to ensure data consistency. To create such a seman-tic reference different facets of data semantics have to beconsidered. In this article three facets are roughly distin-guished:◦ The instance level , referring to the semantics of individual

dataobjects,whichcorresponds to themeaningof entriesin a database.

◦ The type level, designating the semantic classication of data objects, which roughly corresponds to the databaseschema.

◦ The context, which refers to the semantic relationshipsthat associate an object with other objects.

To illustrate the difference of these aspects we may con-sider a concept “diagnosis” on the type level, and a par-ticular instance, say “encephalitis”, and the context of thisinstance, which is determined by the patient, the physicianwho made the diagnosis, and other objects that contributeto a particular statement (information).

• Functional integration refers to the meaningful cooperationof functions of different software components. Uncon-trolled data redundancy is often the result of an insufcientfunctional integration of disparate systems. Autonomously

developed systems often overlap in their functionality,partly providing the same or only slightly differing func-tionality. This aggravates integration even if the systemsare already based on common ontologies. In our charac-terization of integration aspects data integration is con-cerned with the consolidation of declarative knowledge,while functional integration is concerned with the consoli-dation of procedural knowledge on which applications arebased. Both aspects have to be considered for applicationintegration.

• Desktop integration or presentationintegration refersto theuserinterface of a distributed system. Desktop integration isaimed at user transparency, meaning that the user would

notknow what application wasbeing used or whatdatabasewas being queried [14]. This requires more than a uniedlayout and uniform interaction mechanisms. Examples forfunctions needed to achieve desktop integration are “singlesign-on” and “desktop synchronization”. Desktop synchro-nization is needed when a user has multiple windows todifferent applications on her desktop that share a commoncontext. Synchronization is required when the context ischanged in one of the interlinked applications.

Another orthogonal aspect of integration standards is theirscope. We can distinguish between technical and semanticintegration. By “technical integration” we refer to the tech-



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Table 1 – A classication of integration standards

Technicalintegration

Semanticintegration

Data integration Syntacticframeworks

Ontology andvocabulary

Functional integration Middleware Application

frameworks

nical infrastructure which supports application integration.“Semantic integration”, in contrast, refers to the meaning of data and functions. By contrasting the scope with data andfunctional integration we receive a rough matrix that helps tocharacterize different integration standards ( Table 1 ). Subse-quently we explain how different standards can be positionedinto this matrix.

Desktop integration has not been explicitly mentioned inour matrix, because standards supporting desktop integra-tion cover both functional and data integration aspects. Thearchitecture of a distributed system must adhere to certainrequirements, such as a central context manager, in order toenabledesktop synchronization.Moreover, theapplications tobe synchronized must agree on the semantics of context dataand on a common coordination protocol for context synchro-nization. Note, that the remaining distinction between dataintegration and functional integration corresponds to the wellknown distinctionbetweendeclarativeandprocedural knowl-edge, respectively.

4. Results

XML and RDF are examples for standard syntactic frameworkssupporting data integration [15]. Standards for semantic inte-

gration in healthcare are increasingly based on XML in orderto improvesyntactical compatibility withcommonlyaccepteddata processing formats.

Middleware standards typically provide a common infras-tructure for interconnecting distributed software compo-nents. Such standards are primarily intended to provide pro-gramming abstractions, which help a programmer to easilybridge different hardware, operating systems, and program-ming languages. Examples for standardization efforts in thisarea are CORBA, .net, EJB, or Web Services.

Ontologies and vocabulary standards support semantic dataintegration, as they serve as a semantic reference for systemprogrammers and users (cf. [16]). Considering the different

facetsof data integration wendthat well acceptedstandardslike HL7 V2 and DICOM are primarily concerned with orga-nizational issues on a type level and rarely provide meansfor terminological control on the instance level. Newly aris-ing standards like CDA [17,18]and DICOM SR are intended tosupport the interchange of medical contents also on the typeand context levels. There are numerous standards that sup-port terminological control for medical issues at an instancelevel, and the necessity to interlink such terminological stan-dards with the data denitions in HL7 and other type levelstandards has been widely recognized (e.g. [19,20]). Practicalexamples demonstrate how to effectively combine differentstandards on the type and instance level in order to gain more

comprehensive semantic compatibility [21]. The German SCI-PHOX project provides a practical example of how to combineCDA with standards on the terminological level [22].

Despite well accepted standards for data integration likeHL7 V2 and DICOM, healthcare applications are still far fromplug and play compatibility. One reason for this is thatthe existing standards do not address functional integration

issues sufciently. Autonomously developed system compo-nents typically overlap in their functionality and it is notclear,how differentcomponents should interact in order to performa common task. In order to avoid these difculties commonapplication frameworks are required which serve as an addi-tional semanticreference for programmers to create function-ally compatible software components. Requirements for suchan application framework directed towards open systems inthe healthcare domain are described in [23]. In general sucha framework must provide clear specications of interfacesand interaction protocols which are needed for embeddinga software component into a system of cooperating compo-nents. The best example for such a standard in the healthcaredomain is the IHE initiative (“Integrating the Healthcare Enter-prise”) [24]. IHEdoes notdevelopnew standards for data inter-change but species integration proles on the basis of HL7V2 and DICOM. Thereby “actors” and “transactions ” are denedindependently from any specic software product. An inte-gration prole species how different actors interact via IHEtransactions in order to perform a special task. These inte-gration proles serve as a semantic reference for applicationprogrammers, so that they can build software products thatcan be functionally integrated into an IHE conformant appli-cation framework.

HL7 V3 will also take a step into this direction, as confor-mance to HL7 V3 is specied in terms of “application roles”[25]. Like IHE actors, an application role is associated with

somededicated functionality(e.g. “laborder sender”)—it com-prises a set of trigger events, messages and data elementswhich are needed to integrate an IT component with thisfunctionality. An IT component will typically ll many suchapplication roles.

Fig. 1 shows a rough characterization of standards accord-ing to our classication matrix. The position of HL7 in thisdiagram refersto HL7V2. Some improvements that come withHL7 V3 and related standards are roughly indicated by circlesfor RIM, CDA and CCOW[26]. The intention of the diagramis not to precisely and comprehensively classify the differ-ent standards but to get an idea which aspects of semanticintegration are typically covered by such standards. It turns

out that there is a gap in the lower right corner where stan-dardized medical processes could have been expected (suchas IHE addresses organizational processes). Medical pathwaysand guidelines fall into this category. This is essentially med-ical knowledge which has to be consented by medical expertsand which evolves over time. Consented medical knowledgeis necessary for cooperative patient treatment, but it is prob-ably unsuitable as a subject of standardization, as it evolvesrapidly. Note that common formats for knowledge represen-tation (e.g., Arden Syntax [27], GLIF[28,29]PROforma [30], EON[31], Asbru [32], etc.—surveys in [33–36]) are related to thisproblem, but do not ll this gap, as they typically only providestandardized syntactical frameworks for medical knowledge.




Though such common knowledge representation formats arean essential precondition for knowledge exchange among dis-parate systems, semantic integration is not addressedby them.The data denitions in pre-dened formal guidelines may notmap to the data available in an existing electronic healthrecord system [37] unless additional standards for semanticdata integration on type and instance level are considered.

In practice very often operational systems have to be modi-ed and extended in order to acquire the necessary informa-tion needed for guideline implementation. Moreover, effec-tively implementingguidelines in a specic healthcare settingrequires a careful and coordinated evolution of both medicaltreatment processesand embeddedhealthcareapplications inorder to support organizational learning [38]. Standards canonly support generic process patterns which remain stableover longer periods of time. In order to provide adequate andembedded decision support IT systems must be capable of exibly adapting to new requirements.

Fig. 1 alsocontains numerous standards for medical termi-nology. Yet, despite of many attempts, a unique and compre-hensive ontology of the medical domain is not within sight.A closer look at the given examples would reveal that medi-cal terminologies continuously evolve over time (cf. [39]), andthat there is no stable unique reference for system program-mers. Thus, semantic integration of heterogeneous systemsin healthcare will have to deal with volatile medical concepts.

5. Discussion and conclusions

Different kinds of standards are necessary to ease systemsintegration. In particular, both reference ontologies and appli-cation frameworks are needed to support semantic integra-tion. Yet, standards should not try to comprehensively model

an application domain, because systems must be capable torapidly adapt to an evolving application domain. If IT systemsshould bringmedical knowledge to thepoint of care they mustbe capable of incorporating the results of ongoing consensusprocesses among healthcare professionals intoa concreteset-ting [40]. Thus, theevolution of informationsystems should be

a demand-driven process under the control of healthcare pro-fessionals. Process integration is concerned with the alignmentof IT systems to actual business processes in a concrete set-ting. This is not addressed by standards, but by appropriatemodels for demand-driven software development (e.g. [41]).Desiderata for such a demand-driven process are.

• Rapid application development: In order to be able to ex-ibly react to newly arising demands, tools and techniquesfor rapid application development (RAD) are desirable. Toreuse existing data and services and to achieve integratedapplications, such tools should be built upon a standard ITinfrastructure for healthcare networks.

• Robust and stable integrated domain-specic IT infrastruc-ture: An IT infrastructure for a healthcare network shouldprovide a robust and stable basis for application develop-ment. Thus, the framework should be based on generic butstabledomain models insteadofcomprehensive butvolatiledomain models.

• Separation of domain concepts and system implementa-tion: In order to cope with domain evolution, modelingof domain concepts should be separated from IT systemimplementation. IT systems should be implemented by ITexperts and medical knowledge should be modeled andmaintained by domain experts. Yet, separating the model-ing of medical knowledge from implementing an IT infras-tructure is not easy, because algorithms (such as remindersystems) typically refer to medical knowledge in order tofulll their task.

• Multi-level software engineering approach: To bring appli-cation development as close to the end user as possible, amulti-layered software engineering approach is proposed.An idealizedabstractmodel for such a multi-levelapproachfor softwareengineering is shown in Fig. 2. The basic idea is

to distinguish different competencies and different respon-sibilities on the different layers of system design. The aimis to reduce complexity within each layer and to providereusable services for higher layers.

• Layered ontologies: To support semantic integration withinsuch a layered approach, layered ontologies are needed,

Fig. 1 – Contribution of different standards to application integration.




Fig. 2 – A layered approach for system evolution.

which may serve as semantic references on different levelsof software development. The layered approach of the clin-ical document architecture (CDA), and the generic HL7 V3reference information model (RIM) are emerging standardswhich are already built on this fundamental principle (cf.[19]). Yet, these standards are not explicitly aimed at sup-porting a multilevel software-engineering process as sug-gested here. This would require to assign responsibilities tothe different layers. However, the layered approach of CDAalready helps to incrementally improve semantic compati-

bility withinanevolving healthcare informationsystem andto support a stepwise migration process.

Layered approaches have proven to be a successful tech-nique for separating concerns and reducing system complex-ity (cf. [42,43]). Transferring this principle to the developmentof information systems in complex application domains isaimed at allowing application developers and end users tobuild well integrated healthcare applicationswithout theneedto do low level coding and debugging [41]. Appropriate toolsupport is neededat each levelof abstraction in order to effec-tively make use of the lower system layers.

A layered approach, as sketched above, fosters a system

evolution process that follows the principle of “deferred sys-tems design” [44], which is aimed at closing the gap betweensystems design and healthcare process reality [45]. Volatileconcepts are not pre-modeled and hard-coded in software,instead knowledge can be added or modied on demand andat runtime, as the domain evolves.

We have presented a layered model which can be usedas an abstract reference model for evolutionary informationsystems. An example for an adaptation of this model to areal world hospital information system on the basis of com-mercially available system components is given in [41]. Thisadaptation is limited in several respects: as commercial ven-dors typically do not adhere to standard architectural layer-

ingsa proprietary vendor-specicapproachhad to be adopted.This approach allows for site-specic demand-driven soft-ware development on the basis of a generic core system. Theapproachsupports bothsystem integrationand system evolu-tion at the cost of vendor dependency and limited expressive-ness. Semanticintegrationin widespreadhealthcarenetworkswill necessarily require more general approaches, as single-vendor solutions are not applicable to fully support cross-organizational processes.

Multi-layered service-oriented architectures are expected

to provide a suitable technical platform for IT support in het-erogeneous healthcare networks, as they provide the nec-essary technical infrastructure for loosely coupled interop-erating components [1,46]. Generic healthcare-specic ser-vices could be implemented on the basis of this infras-tructure providing a stable domain-specic platform for dis-tributed healthcare applications. Examples for core servicesmight include patient identication and lexical query. Thelatter can help to separate terminological control from appli-cation development. A successful proprietary example of aterminology server is the Medical Entities Dictionary (MED)fromColumbia PresbyterianMedical Center [47,48]. Functionalinterfaces for such services have alreadybeenproposedin var-

ious attempts aimed at standardization of healthcare-specicmiddleware (e.g. HISA [49] or CORBAmed [50]). Research pro-totypes based on these recommendations have already beendeveloped [51].

In addition to such a basic healthcare-specic serviceinfrastructure there is a need for concepts that help to sep-arate different layers of software engineering, as differentresponsibilities and competencies are to be addressed fortechnological evolution, domain evolution, and site-specicadaptation. A rst step towards such a separation of con-cerns is provided by the “archetype” approach, which hasbeen developed in the context of the GEHR project [52,53].This concept is aimed at separating IT systems from domain




knowledge, in order to enable medical knowledge to be mod-eled by domain experts rather than IT specialists. Archetypesare focused on the specication of declarative medical knowl-edge. However, as we have seen in our discussion of stan-dards, procedural knowledge is equally important for seam-less integration and it also evolves rapidly. Thus, in addi-tion to standard formats for guideline representation we

also need architectural approaches that allow for separatespecication of medical guidelines. Such issues have beenaddressed in the context of research projects like EON [54]and GUIDE[55], which strictly follow the idea of separation of concerns.

Despite of many promising approaches there are stillnumerous challenges to solve before comprehensive evo-lutionary information systems for healthcare networks canbecome a reality. To develop future-proof IT platforms weshould keep in mind that these systems are part of complexsocio-technical systems which can only be effective if theyexibly support organizational learning.

r e f e r e n c e s

[1] M. Beyer, K.A. Kuhn, C. Meiler, S. Jablonski, R. Lenz, in: H.Haddad, A. Omicini, R.L. Wainwright, L.M. Liebrock (Eds.),Towards a Flexible, Process-oriented IT Architecture for AnIntegrated Healthcare Network, ACM, 2004, pp. 264–271.

[2] G. Alonso, F. Casati, H. Kuno, V. Machiraju, WebServices—Concepts, Architectures and Applications,Springer, Berlin, 2003.

[3] J.T. Pollock, The Web Services Scandal—How Data SemanticsHave Been Overlooked in Integration Solutions, EAI J. (2002)20–23.

[4] A. Sheth, J. Larsen, Federated database systems formanaging distributed, heterogeneous, and autonomous

databases, ACM Comput. Surv. 22 (3) (1990) 183–235.[5] M. Stonebraker, Integrating islands of information, EAI J.(1999) 1–5.

[6] A. Elmagarmid, M. Rusinkiewicz, A. Sheth (Eds.),Management of Heterogeneous and Autonomous DatabaseSystems, Morgan Kaufmann Publishers, San Francisco,California, 1999.

[7] S. Conrad, Schemaintegration-Integrationskonikte,L osungsans atze, aktuelle Herausforderungen, InformatikForschung Entwicklung (17) (2002) 101–111.

[8] A. Bouguettaya, B. Benatallah, A. Elmagarmid,Interconnecting Heterogeneous Information Systems,Kluwer Academic Publishers, Boston, 1998.

[9] E. Rahm, P.A. Bernstein, A survey of approaches to automaticschema matching, VLDB J. 2001 (10) (2001) 334–350.

[10] C. Batini, M. Lenzerini, S.B. Navathe, A comparative analysisof methodologies for database schema integration, ACMComput. Surv. 18 (4) (1986) 323–364.

[11] S. Heiler, Semantic interoperability, ACM Comput. Surv. 27(2) (1995) 271–273.

[12] R.M. Colomb, Impact of semantic heterogeneity onfederating databases, Comput. J. 40 (5) (1997) 235–244.

[13] R. Lenz, M. Beyer, C. Meiler, S. Jablonski, K.A. Kuhn,Informationsintegration inGesundheitsversorgungsnetzen-Herausforderungen an dieInformatik, Inform. Spekt. 28 (2) (2005) 105–119.

[14] B.T. Pille, R.K. Antczak, Application integration, in: M.J. Ball, J.V. Douglas (Eds.), Performance Improvement ThroughInformation Management, Springer, New York, 1999, pp.144–152.

[15] H. Sch oning, XML und Datenbanken—Konzepte undSysteme, Carl Hanser Verlag, M unchen, 2003.

[16] R. Lenz, K.A. Kuhn, Intranet meets hospital informationsystems: the solution to the integration problem? Meth.Inform. Med. 40 (2) (2001) 99–105.

[17] R.H. Dolin, L. Alschuler, C. Beebe, P.V. Biron, S.L. Boyer, D.Essin, et al., The HL7 clinical document architecture, J. Am.Med. Inform. Assoc. 8 (6) (2001) 552–569.

[18] R.H. Dolin, L. Alschuler, S. Boyer, C. Beebe, F.M. Behlen, P.V.Biron, et al., HL7 clinical document architecture, release 2, J.Am. Med. Inform. Assoc. 13 (1) (2005) 30–39.

[19] G.W. Beeler, HL7 version 3—an object-oriented methodologyfor collaborative standards development, Int. J. Med. Inform.48 (1–3) (1998) 151–161.

[20] P.E. Zanstra, A.L. Rector, W. Ceusters, P.F. de Vries Robbe,Coding systems and classications in healthcare:the link to the record, Int. J. Med. Inform. 48 (1–3) (1998)103–109.

[21] J.F. Coyle, A.R. Mori, S.M. Huff, Standards for detailed clinicalmodels as the basis for medical data exchange and decisionsupport, Int. J. Med. Inform. 69 (2–3) (2003) 157–174.

[22] K.U. Heitmann, R. Schweiger, J. Dudeck, Discharge andreferral data exchange using global standards—the SCIPHOXproject in Germany, Int. J. Med. Inform. 70 (2–3) (2003)195–203.

[23] R. Lenz, S. Huff, A. Geissbuhler, Report of conference track.2. Pathways to open architectures, Int. J. Med. Inform. 69(2–3) (2003) 297–299.

[24] P. Vegoda, Introducing the IHE (Integrating the HealthcareEnterprise) concept, J. Health Inform. Manag. 16 (1) (2002)22–24.

[25] Health Level Seven I. HL7 Version 3 Statement of Principles.Health Level Seven Inc., URL:http://www.hl7.org/Library/data-model/SOP 980123 nal.zip .

[26] R. Seliger, Overview of HL7’s CCOW Standard. Health LevelSeven Inc., URL: http://www.hl7.org/library/committees/sigvi/ccow overview 2001.doc .

[27] R.A. Jenders, G. Hripcsak, R.V. Sideli, W. DuMouchel, H.

Zhang, J.J. Cimino, et al. Medical decision support:experience with implementing the Arden Syntax at theColumbia—Presbyterian Medical Center. In: Proceedings of the Annual Symposium on Comput. Appl. Med. Care, 1995,pp. 169–173.

[28] L. Ohno-Machado, J.H. Gennari, S.N. Murphy, N.L. Jain, S.W.Tu, D.E. Oliver, et al., The guideline interchange format: amodel for representing guidelines, J. Am. Med. Inform.Assoc. 5 (4) (1998) 357–372.

[29] M. Peleg, A.A. Boxwala, O. Ogunyemi, Q. Zeng, S. Tu, R.Lacson, et al., GLIF3: the evolution of a guidelinerepresentation format, Proc. AMIA Symp. (2000)645–649.

[30] J. Fox, N. Johns, A. Rahmanzadeh, Disseminating medicalknowledge: the PROforma approach, Artif. Intell. Med. 14

(1–2) (1998) 157–181.[31] M.A. Musen, Domain ontologies in software engineering:use of Protege with the EON architecture, Meth. Inf. Med. 37(4–5) (1998) 540–550.

[32] Y. Shahar, S. Miksch, P. Johnson, An intention-basedlanguage for representing clinical guidelines, Proc. AMIAAnnu. Fall Symp. (1996) 592–596.

[33] P.A. de Clercq, J.A. Blom, H.H. Korsten, A. Hasman,Approaches for creating computer-interpretable guidelinesthat facilitate decision support, Artif. Intell. Med. 31 (1)(2004) 1–27.

[34] R. Van de Velde, P. Degoulet, Clinical Information Systems,Springer-Verlag, New York, 2003.

[35] M. Peleg, S. Tu, J. Bury, P. Ciccarese, J. Fox, R.A. Greenes, et al.,Comparing computer-interpretable guideline models: a




case-study approach, J. Am. Med. Inform. Assoc. 10 (1) (2003)52–68.

[36] P. Votruba, S. Miksch, R. Kosara, Facilitating knowledgemaintenance of clinical guidelines and protocols,Medinformation (2004) 57–61.

[37] T.A. Pryor, G. Hripcsak, Sharing MLM’s: an experimentbetween Columbia-Presbyterian and LDS Hospital. In:Proceedings of the Annual Symposium on Comput. Appl.

Med. Care, 1993, pp. 399–403.[38] R. Lenz, M. Reichert, IT support for healthcareprocesses—premises, challenges, perspectives. Data Know.Eng., in press.

[39] C.J. McDonald, J.M. Overhage, P. Dexter, B. Takesue, J.G.Suico, What is done, what is needed and what is realistic toexpect from medical informatics standards, Int. J. Med.Inform. 48 (1–3) (1998) 5–12.

[40] R. Lenz, M. Reichert, IT support for healthcare processes, in:W. van der Aalst, B. Benatallah, F. Casati, F. Curbera (Eds.),Business Process Management, Springer, Berlin, 2005, pp.354–363.

[41] R. Lenz, K.A. Kuhn, Towards a continuous evolution andadaptation of information systems in healthcare, Int. J. Med.Inform. 73 (1) (2004) 75–89.

[42] T. H arder, Realisierung von operationalen Schnittstellen, in:P.C. Lockemann, J.W. Schmidt (Eds.), Datenbank-Handbuch,Springer-Verlag, Berlin,1987.

[43] A.S. Tanenbaum, Computer Networks. 2nd. Au.,Prentice-Hall, Englewood Cliffs, NJ, 1988.

[44] N. Patel, Adaptive Evolutionary Information Systems, IdeaGroup Publishing, London, 2003.

[45] R. Heeks, Health information systems: Failure, successand improvisation, Int. J. Med. Inform. 75 (2) (2006) 125–137.

[46] J. Mykkanen, J. Porrasmaa, J. Rannanheimo, M. Korpela, Aprocess for specifying integration for multi-tier applicationsin healthcare, Int. J. Med. Inform. 70 (2–3) (2003) 173–182.

[47] B.H. Forman, J.J. Cimino, S.B. Johnson, S. Sengupta, R. Sideli,P. Clayton, Applying a controlled medical terminology to adistributed, production clinical information system, in:Proceedings of the Annual Symposium on Comput. Appl.Med. Care, 1995, pp. 421–425.

[48] J.J. Cimino, From data to knowledge throughconcept-oriented terminologies: experience with theMedical Entities Dictionary, J. Am. Med. Inform. Assoc. 7 (3)(2000) 288–297.

[49] J.R. Scherrer, S. Spahni, Healthcare information systemarchitecture (HISA) and its middleware models, in:Proceedings of AMIA Symposium, 1999, pp. 935–939.

[50] B. Blobel, M. Holena, Comparing middleware concepts foradvanced healthcare system architectures, Int. J. Med.Inform. 46 (2) (1997) 69–85.

[51] N. Saranummi, PICNIC architecture, Stud. Health Technol.Inform. (2005) 37–60.

[52] T. Beale, Archetypes and the EHR, Stud. Health Technol.Inform. 96 (2003) 238–244.

[53] T. Beale, Archetypes: constraint-based domain models forfuture-proof information systems. In: OOPSLA 2002Workshop on Behavioural Semantics, 2002.

[54] M.A. Musen, Y. Shahar, E.H. Shortliffe, Clinical decisionsupport systems, in: E.H. Shortliffe, L.E. Perreault, G.Wiederhold, L.M. Fagan (Eds.), MedicalInformatics—Computer Applications in Healthcare andBiomedicine, Springer, New York, 2000, pp. 573–609.

[55] P. Ciccarese, E. Caf, S. Quaglini, M. Stefanelli, Architecturesand tools for innovative Health Information Systems: theGuide Project, Int. J. Med. Inform. 74 (7–8) (2005) 553–562.

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37-Semantic Integration in Healthcare Networks