International Journal of Medical Informatics
Semantic integration in healthcare networks
Introduction
Healthcare increasingly changes from isolated treatment episodes towards a continuous treatment process involving multiple healthcare professionals and various institutions. This change motivates comprehensive, inter-institutional IT support in health information systems and imposes new demanding requirements for IT [1]. IT applications should guide data acquisition in a way that data are placed in a meaningful context from the beginning, so that they are ready for reuse in different contexts without the need to manually index or transform the data. To achieve such an IT support, heterogeneous IT systems have to be integrated into a comprehensive distributed information system. Integrating autonomous software components, however, is a difficult task, as individual applications usually are not designed to cooperate. Applications are often based on differing conceptualizations of the application domain. Today powerful integration tools (e.g. application servers, object brokers, different kinds of message-oriented middleware, and workflow management systems [2]) are available to overcome technical and syntactical heterogeneity of autonomous system components. Yet, semantic heterogeneity remains as a major barrier to seamless integration of autonomously developed software components (cf. [3]). Semantic heterogeneity occurs when there is disagreement about the meaning, interpretation or intended use of the same or related data [4]. It occurs in different contexts, like database schema integration, ontology mapping, or integration of different terminologies. The underlying problems are more or less the same, though they are often complex and still poorly understood. Stonebraker characterizes disparate systems as “islands of information” and points out two major factors which aggravate systems integration [5]:
- 1.
Each island (i.e. application) will have its own meaning of enterprise objects.
- 2.
Each island will have data that overlaps data in other islands. This partial redundancy generates a serious data integrity problem.
Based on this statement, data integration can be led back to a mapping problem (how to map different conceptualizations in a semantically correct way) and a synchronization problem (how to ensure mutual consistency of redundant data which are stored in different databases under the control of autonomous applications). The mapping problem is essentially related to the schema integration problem of database systems, which has been extensively discussed in the database literature in recent years (e.g. [6], [7], [8], [9]). A major perception in data integration research has been that schema integration cannot be automated in general. In [10] it is stated: “The general problem of schema integration is undecidable.” Heiler states that “understanding data and software can never be fully automated” [11]. As a consequence, the process of schema integration always needs a human integrator for certain semantic decisions. Colomb even goes a step further by stating that there are cases where no consistent interpretation of heterogeneous sources is possible (“fundamental semantic heterogeneity”) [12]. In such cases one either has to accept a low degree of data quality, or systems have to be modified to resolve fundamental semantic heterogeneity.
In order to reduce the integration efforts caused by semantic heterogeneity standards for systems integration are needed. Moreover, as medicine is a rapidly evolving domain, concepts for system evolution are needed. Fortunately, there are already far reaching standards that support information interchange in the medical domain. Yet, healthcare software is still far away from plug and play compatibility, and systems integration is typically a difficult process. In a research project in which we focus on the development of a reference architecture for comprehensive information systems in healthcare networks [1], [13], we have identified concurring and semantically overlapping standards. To get an overview of the standards’ characteristics and interrelations, we have arranged them to a system of standards which we find to be helpful for architecture development.
Section snippets
Objectives
In this article we try to clarify how different standards contribute to systems integration by distinguishing different aspects 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 is responsible for the high effort for systems integration. The goal of this clarification is to derive recommendations for future system architectures and standards development.
Methods
At a conceptual level, information systems are designed around three layers: presentation, application logic, and resource management [2]. According to this well known abstract model of information systems, we distinguished different aspects of integration: data integration, functional integration and presentation integration:
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Data integration: we have already characterized semantic heterogeneity as the main cause for high integration efforts. We thereby focused on data integration. The reason
Results
XML and RDF are examples for standard syntactic frameworks supporting data integration [15]. Standards for semantic integration in healthcare are increasingly based on XML in order to improve syntactical compatibility with commonly accepted data processing formats.
Middleware standards typically provide a common infrastructure for interconnecting distributed software components. Such standards are primarily intended to provide programming abstractions, which help a programmer to easily bridge
Discussion and conclusions
Different kinds of standards are necessary to ease systems integration. In particular, both reference ontologies and application frameworks are needed to support semantic integration. Yet, standards should not try to comprehensively model an application domain, because systems must be capable to rapidly adapt to an evolving application domain. If IT systems should bring medical knowledge to the point of care they must be capable of incorporating the results of ongoing consensus processes among
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