A Framework for Interoperable Healthcare Information Systems

Abstract: It is clear that in today's organizations, new and existing applications require access to data stored in several preexisting databases held at several local and remote locations. Therefore, a main criterion required by most complex organizations, is the provision of collaboration possibilities and information integration mechanisms among distributed, heterogeneous, and autonomous database systems. The development of an application provides interoperability and information integration among distributed systems, via the deployment of database standards and emerging Internet technologies. It is one of the most challenging approaches in the area of integrating heterogeneous information from autonomous sites. In this context, the work described in this paper focuses on the design and development of a Generic Information Exchange (GIE) System. The system supports a wide variety of applications with efficient means for their interconnection and interoperation, while preserving their heterogeneity, distribution, and full autonomy. An example of the interoperability problem is found in the healthcare domain, where each hospital, or even each department in a hospital, maintains its own database. In this environment it is very important to permit users to locate and access data from several remote databases, supporting the needs of patient care, daily operations of the hospitals and research consultations. It necessitates the sharing and exchange of data related to clinical, administrative, managerial and research (statistical) information. Therefore, it is necessary to propose an approach i.e. GIE System that permits interoperation among heterogeneous, distributed, and autonomous sites

SEMANTIC INTEROPERABILITY AND DATA MAPPING IN EHR SYSTEMS

The diversity in representation of medical data prevents straightforward data mapping, standardization and interoperability between the heterogeneous systems. We identify a specific problem, namely the need to achieve interoperability by applying a standard based data modeling approach to achieve a common platform that serves to improve the health data mapping of unstructured data and addresses ambiguity issues when dealing with health data from heterogeneous systems. In this thesis, we proposed an original Hybrid algorithm that identifies the attributes of data in heterogeneous systems based on critical medical standards and protocols and then performs semantic integration to form a uniform interoperable system. Also, efficient data modeling techniques are introduced for improving data storage and extraction. We tested the proposed algorithm with multiple data sets and compared the proposed approach with traditional data modeling approaches. We found that the proposed approach demonstrated performance improvements and reduction in data losses