Sensor Grid Enhancement with Data Management System for Ubiquitous Healthcare Computing

A Wireless Sensor Network (WSN) can be deployed to monitor the health of patients suffering from critical diseases. A wireless network consisting of biomedical sensors can also be implanted into the patient's body and can monitor the patients' conditions. These sensor devices, apart from having an enormous capability of collecting data from their physical surroundings, are also resource constraint in nature with a limited processing and communication ability. Therefore, it is necessary to integrate them with the Grid technology in order to process and store the collected data by the sensor nodes. This chapter proposes the SEnsor Grid Enhancement Data Management system, called SEGEDMA, ensuring the integration of different network technologies and the continuous data access to system users. The main contribution of this work is to achieve the interoperability of both technologies through a novel network architecture ensuring also the interoperability of Open Geospatial Consortium (OGC) and HL7 standards. According to the results SEGEDMA can be applied successfully in a decentralized healthcare environment.

On the Integration of Clinical Archetypes with Ontologies and Rules

This chapter presents an approach to translate definitions expressed in openEHR Archetype Definition Language (ADL) to a formal representation using ontology languages. The approach is implemented in the ArchOnt framework, which is also described. The integration of those formal representations with clinical rules is then studied, providing an approach to reuse reasoning on concrete instances of clinical data. Sharing the knowledge expressed in the form of rules is coherent with the philosophy of open sharing underlying clinical archetypes, and it also extends reuse to propositions of declarative knowledge as those encoded for example in clinical guidelines. Thus, this chapter describes the techniques to map archetypes to formal ontologies and how rules can be attached to the resulting representation. In addition, the translation allows specifying logical bindings to equivalent clinical concepts from other knowledge sources. Such bindings encourage reuse as well as ontology reasoning and navigability across different ontologies. Another significant contribution of the chapter is the application of the presented approach as part of two research projects in collaboration with teaching hospitals in Madrid. Examples taken from those cases, such as the development of alerting systems aimed at improving patient safety, are explained. Besides the direct applications described, the automatic translation of archetypes to an ontology language fosters a wide range of semantic and reasoning activities to be designed and implemented on top of a common representation instead of taking an ad-hoc approach.

Standards Related to Interoperability in EHR & HS

The standardization of clinical data represents a major step in the development of information and organizational knowledge of health services. The evolution of information systems from a model of different database owners to a different open software based model is a major challenge. For this reason it is essential to adopt models of metadata based on archetypes to improve the development of information systems and simultaneously integrate all applications. The adoption of clinical terminology that can translate existing knowledge and enhance its growth is a necessary goal. Accessibility, ubiquity, completeness, consistency and durability of the clinical data are essential objectives for efficiency and effectiveness gains in organizations. This chapter presents the concepts and technologies needed to implement a model of EHR (Electronic Health Record) based on a standard, open architecture. It also presents some concepts of decision support systems and business processes that can be integrated with the EHR.

Semantic Integration of Patient Data for Clinical Decision Support in Breast Cancer Care

Achieving semantic interoperability between heterogeneous healthcare systems and integrating clinical guidelines in the automatic decision support of healthcare institutions are two key priorities of current medical informatics. They can lead to a significant improvement on patient safety by reducing medical risks and delays in diagnosis, facilitating continuity of care and preventing life threatening adverse events. The present chapter describes a project that addresses those two priorities in the field of Breast Cancer for which effective clinical guidelines are available, as well as the clinical data to apply them. However, the deployment of semantic interoperability techniques based on clinical terminologies such as SNOMED-CT and EHR exchange models such as openEHR and HL7 is required to meaningfully combine the available data. Then data mining techniques are capable of automatically adapting the parameters of clinical guidelines to the particular conditions of each healthcare environment.

Using Agent and Workflow Technologies for the Implementation of Interoperable Healthcare Information Systems

Healthcare is an increasingly collaborative enterprise involving many individuals and organizations that coordinate their efforts toward promoting quality and efficient delivery of healthcare through the use of interoperable healthcare information systems (HIS). Service-oriented architecture (SOA) provides a cost-effective solution to implementing interoperability between heterogeneous HIS which have resulted from extensive investments that most healthcare organizations have made in system resources over the course of many years. However, issues of semantic interoperability still remain unresolved while new challenges arise regarding web service interoperability. This chapter presents a mediator-based approach for achieving data and service interoperability among disparate and geographically dispersed HIS. The proposed system architecture provides a uniform interface to the underlying HIS, thus enabling decoupling of the client applications and the server-side implementations while it ensures security in all transactions. It is a distributed system architecture based on the agent paradigm for both healthcare process management and management of interactions among the participating systems. The healthcare processes and all interactions involved in each process are described according to the workflow metaphor. Thus, robustness, high flexibility and fault tolerance are provided in an environment as dynamic and heterogeneous as healthcare.

A Formal Investigation of Semantic Interoperability of HCLS Systems

Semantic interoperability facilitates Health Care and Life Sciences (HCLS) systems in connecting stakeholders at various levels as well as ensuring seamless use of healthcare resources. Their scope ranges from local to regional, national and cross-border. The use of semantics in delivering interoperable solution for HCLS systems is weakened by fact that an Ontology Based Information System (OBIS) has restrictions in modeling, aggregating, and interpreting global knowledge in conjunction with local information (e.g., policy, profiles). This chapter presents an example-scenario that shows such limitations and recognizes that enabling two key features, namely the type and scope of knowledge, within a knowledge base could enhance the overall effectiveness of an OBIS. This chapter provides the idea of separating knowledge bases in types with scope (e.g., global or local) of applicability. Then, it proposes two concrete solutions on this general notion. Finally, the chapter describes open research issues that may be of interest to knowledge system developers and broader research community.

A Multi-Tiered Perspective on Healthcare Interoperability

The current healthcare delivery paradigm is defined by integrative care delivery across disparate providers and services. Therefore the ability to deliver efficient and effective healthcare services is dependent on designing and implementing interoperable systems. However, the notion of interoperability is multifaceted and complex. Although the exchange of data is often described as analogous with interoperability it must be remembered that healthcare is a process oriented domain and clinical, management, organizational and other processes must be considered as part of interoperability. This chapter discusses healthcare delivery and the role interoperability plays in supporting its delivery. First, the chapter provides a background on healthcare interoperability from multiple perspectives. Then it presents a case study of collaborative care delivery and uses it to outline specific interoperability requirements. The chapter then uses these requirements to develop a multi-tiered framework of healthcare interoperability, concluding with a discussion of the implications of the framework for interoperability research and for systems design to support integrated healthcare delivery.

Interoperability of EHR Systems Based on Semantic Representation and Transformation Models

The semantic interoperability of electronic healthcare record systems is a major issue given that it would increase the quality of healthcare and patient safety. In this chapter, different solutions based on the use of semantic models for representing clinical knowledge are presented. The aim of the research presented here is to contribute to the achievement of semantic interoperability by making explicit the meaning of the exchanged data and knowledge items. In particular, this work is focused on dual model-based electronic healthcare records standards and presents methods and tools for the representation and transformation of clinical archetypes and for the automatic generation of standardized applications.

Enabling Interoperability of Patient Summaries across Europe with Triplespaces

Healthcare services are naturally distributed, both from a geographical and an organisational point of view; they include various national and local authorities, as well as different actors ranging from general hospitals to individual physicians. One of the main items on the eHealth agenda of the European Union is the establishment of electronic patient summaries as an instrument to facilitate the pervasive delivery of healthcare and to guarantee its continuity across national boundaries. This chapter analyses several existing large-scale eHealth systems, chosen among the most prominent and established ones in Europe, and analyses their ability to cope with the high heterogeneity challenges of a Europe-wide solution. It then introduces triplespaces, a new-generation middleware based on the concept of semantic spaces, and presents a possible realisation of a European Patient Summary based on a triplespace infrastructure. The chapter shows that this solution can cope with the decentralisation, asynchronicity, subsidiarity, and security requirements arising in the European Patient Summary setting.

LinkEHR

Normalization of data is a prerequisite to achieve semantic interoperability in any domain. This is even more important in the healthcare sector due to the special sensitivity of medical data: data exchange must be done in a meaningful way, avoiding any possibility of misunderstanding or misinterpretation. In this chapter, we present the LinkEHR system for clinical data standardization and exchange. The LinkEHR platform provides tools that simplify meaningful sharing of electronic health records between different systems and organizations. Key contributions of LinkEHR are the development of a powerful medical concept, expressed in the form of archetypes, editing framework based on formal semantics capable of handling multiple electronic health record architectures, the definition of high-level non-procedural mappings to describe the relationship between archetype and legacy clinical data and the semi-automatic generation of XQuery scripts that transform legacy data into XML documents compliant with the underlying electronic health record data architecture and at the same time satisfy the constraints imposed by the archetype.