Solving the Modeling Dilemma as a Foundation for Interoperability
European Journal for Biomedical Informatics
In order to support highly distributed, personalized, predictive, preventive, participative, and cognitive care healthcare systems have to provide and to ensure reliable environments. The approach requires the exchange of data in a highly interoperable fashion across different disciplines and domains. The involvement of stakeholders from different specialties and policy domains, offering different levels of knowledge, skills, and experiences to act in different scenarios accommodating different
... business cases has to be supported by allowing specific methodologies, terminologies, and ontologies to enable analysis, design, implementation, deployment, maintenance, and evaluation of systems within their lifecycle. The management of such highly dynamic, complex, heterogeneous and context-depending business processes, i.e. the execution of ICT (Information and Communication Technology)-supported business operations from a business process expert's view, must be formalized [1, 2] to enable automation of the business process management. A system-oriented, architecture-centric, ontology-based modeling approach based on ontology languages, repositories, reasoners, and query languages provides methods and tools scalable and adaptive to communities, user groups and even individuals, transferring their knowledge, experience, expectations, and intentions 2 Methods General Aspects of Modeling According to Alter , a model is a partial representation of reality. It is restricted to attributes the modeler is interested Abstract Introduction: Progressive health paradigms, involving many different disciplines and combining multiple policy domains, requires advanced interoperability solutions. This results in special challenges for modeling health systems. Methods: The paper discusses classification systems for data models and enterprise business architectures and compares them with the ISO Reference Architecture. Results and Conclusions: Existing definitions, specifications and standards for data models enabling interoperability are analyzed, and their limitations are evaluated. Amendments to correctly use those models and to better meet the aforementioned challenges are offered.