Dexhelpp –Decision Support for Health Policy: Methods, Models and Technologies based on Existing Health Care Data to Support the Analysis, Planning and Controlling of the Health Care System
Niki Popper & Dexhelpp Team Members
To provide state of the art Health Technology Assessment (HTA), Comparative Effectiveness Research (CER) and Evidence Based Medicine (EBM) we will need to combine health system domain knowledge, knowledge of professional data processes and – last but not least – mathematical modelling & simulation skills. The areas of data security & data management, big data analysis, machine and deep learning, statistical methods, mathematical modelling & simulation and visualisation as well as public health & decision analytics modelling should help us to transform Big Data into Deep Data: evidence based, reproducible knowledge.
To bring together all technologies still is a huge challenge. Data Based Demographic models have to be combined with analysis and models for the spreading of diseases. Time dependent treatment paths have to be parametrized with data sets from clinical routine joined with large scale health system data. From the system simulation point of view an important aspect is the possibility to implement changes inside the system, like interventions inside the computer model, and to analyse their effects. N. Popper tries to point out how big interdisciplinary teams will handle the complex processes in the future and which methods are and aren’t promising.
Nikolas Popper (Director DEXHELPP – Decision Support for Health Policy and Planning, Vienna; Coordinator Centre for Computational Complex Systems, TU Wien; CSO dwh GmbH – Simulation Services and Technical Solutions, Vienna)