Comparison of two treatment methods with regard to their regional age distribution.

Detailed analysis of patient stays with a remarkably long duration.

Detailed analysis of local healthcare indicators as spatial distribution and over time.

Comparison of two treatment methods with regard to their distribution on weekdays per region as well as the time course.

DEXHELPP develops new methodologies for supporting the analysis, planning, and controlling of healthcare systems by combining decision analytics, data security, data management, statistics, mathematical modelling, simulation, and visual analytics.

The goal of the K-project DEXHELPP ( is to develop new methodologies for supporting the analysis, planning, and controlling of healthcare systems. In Austria, the healthcare system costs more than 30 billion Euros per year and the demand for health services is increasing due to demographic changes the advent of new effective health technology. Up to now, the decisions in healthcare system planning are mainly based on the evidence gained from controlled studies rather than on analysing available routine data. DEXHELPP seeks to make this data usable for optimal decision support.

The project consortium includes partners from the public health sector, information technology, statistics, mathematical modelling, and visual computing. This enables to combine approaches in the areas of public health & decision analytics modelling, data security, data management, statistical methods, mathematical modelling and simulation as well as visual analytics. Joining these methods helps to analyse and model the spreading of diseases in the population, to investigate treatment paths, and to study the effects of changes to the healthcare system based on computer models. Specifically, applications include the analysis of different screening programs, the analysis of the primary care, a simulation model to evaluate different real and possible paying systems, assessment of needs based on morbidity, also concerning different regions in Austria, examinations of chronical diseases like diabetes, and the analysis of prevention programs. DEXHELPP develops all necessary processes in order to treat complex data from the Austrian healthcare system in a secure manner, while integrating a new research server at the TU Vienna.

The role of VRVis within the DEXHELPP consortium is to provide expertise in Visual Analytics and to research new visualization technology in order to support the process of data-driven modelling in the healthcare context. Based on the software toolkit Visplore, the Visual Analysis group as headed by Dr. Harald Piringer tightly collaborates with the consortium partners along all stages of the workflow and creates interactive visual tools for data quality assessment, simulation model validation, sensitivity analysis of decision parameters, decision making, and presentation.