No abstract

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper.

BackgroundHealth information exchange and health information integration has become one of the top priorities for healthcare systems across institutions and hospitals. Most organizations and establishments implement health information exchange and integration in order to support meaningful information retrieval among their disparate healthcare systems. The challenges that prevent efficient health information integration for heterogeneous data sources are the lack of a common standard to support mapping across distributed data sources and the numerous and diverse healthcare domains. Health Level Seven (HL7) is a standards development organization which creates standards, but is itself not the standard. They create the Reference Information Model. RIM is developed by HL7's technical committees. It is a standardized abstract representation of HL7 data across all the domains of health care. In this article, we aim to present a design and a prototype implementation of HL7 v3-RIM mapping for information integration of distributed clinical data sources. The implementation enables the user to retrieve and search information that has been integrated using HL7 v3-RIM technology from disparate health care systems.Method and resultsWe designed and developed a prototype implementation of HL7 v3-RIM mapping function to integrate distributed clinical data sources using R-MIM classes from HL7 v3-RIM as a global view along with a collaborative centralized web-based mapping tool to tackle the evolution of both global and local schemas. Our prototype was implemented and integrated with a Clinical Database management Systems CDMS as a plug-in module. We tested the prototype system with some use case scenarios for distributed clinical data sources across several legacy CDMS. The results have been effective in improving information delivery, completing tasks that would have been otherwise difficult to accomplish, and reducing the time required to finish tasks which are used in collaborative information retrieval and sharing with other systems.ConclusionsWe created a prototype implementation of HL7 v3-RIM mapping for information integration between distributed clinical data sources to promote collaborative healthcare and translational research. The prototype has effectively and efficiently ensured the accuracy of the information and knowledge extractions for systems that have been integrated

With current national emphasis on translational research, data exchange systems are needed that bridge basic science and clinical research. To meet this challenge, an electronic system was developed by the Biomedical Informatics Unit (BMIU) of the University of Tennessee Clinical Translation Science Institute (UT CTSI). This integrated data system collects, processes, archives, and distributes basic, clinical, and translational research data. The system provides information via web-based applications in a secure and Health Insurance Portability and Accountability Act (HIPAA)-compliant manner to facilitate data sharing and analysis across domains. The system is currently in use by a number of studies and has proven to be an effective tool for data collection and processing in clinical studies.