Advanced health information sharing with web-based GIS
Abstract
Web-based GIS is increasingly utilized in health organizations to share and visualize georeferenced health data through the Web. In the development of a public information and disease surveillance network, issues of data publishing and user access are important concerns. The handling of data heterogeneity, lack of available data and tools, and methods of health information representation constitute continuing challenges. The purpose of this research is to address these three problems and provide new solutions for health information sharing.
Regarding data heterogeneity, a geospatial-enabled RuleML method has been designed for semantic disease information queries. Geospatial and non-spatial components of health data are represented through an ontology-based approach. The support for spatial representation in the proposed method enables the discovery of spatial relations in a semantic system. This research proposed an improved system, based on ontologies and rules, addressing both non-spatial and geospatial semantics for the querying of respiratory disease information.
Furthermore, a new architecture based on open standards and Web Services was designed to provide better solutions in health information sharing with Web-based GIS. This architecture overcomes the weakness of a closely coupled design, allows interoperable data access, and enables dynamic data integration from different providers for decision making. This architecture has demonstrated its effectiveness in an infectious disease information mapping application across international borders. In addition to demonstrating health information sharing, this research provided an initial approach to designing and implementing Web Processing Services that allow online sharing of health data processing functionalities.
For the dissemination of health information, a health information representation model has been designed to facilitate users’ understanding in using health information. This model covers health information representation in the semantic, geometric, and graphic dimensions with the purpose of minimizing user misunderstanding. The platform-independent XML format was utilized in the implementation of this model, and maps can be generated from this XML format for visualization and analysis.