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Departments >> Faculty of Engineering >> Department of Computer Science >> Chair of Computer Science 6 (Data Management) >>

Project: Horizontale Innovationen zur Produkt- und Prozessoptimierung

Subproject: H-01: Prospective Health Technology Assessment

Module: Information and Knowledge Database

It is the approach of ProHTA to understand the impact of products and solutions on medical and organizational processes already at the beginning of the concept phase. Thereby these processes can be prospectively optimized with the health technologies’ innovative possibilities. By integrating technology and processes in simulated scenarios, impacts on health care players can be derived and analyzed on a cost-efficiency basis. Finally conclusions can be drawn regarding the innovation’s development as well as necessary adaptations of the health care system in general. The overall goal of ProHTA is the implementation of a platform for scientific services at the disposal of the regional health care manufacturers. The platform accumulates, as already mentioned above, knowledge and technical tools to answer two questions.

  • What are effects of innovative health technologies and products on the quality and costs of health care?

  • Where are potentials of efficiency enhancement within the supply chain of health care?

Adaptive evolutionary information systems are fundamental for the success of ProHTA because the data storage requirements will change over time. Data types that are unknown today will turn out to be important for predicting future scenarios. Early project assumptions will need to be revised as our knowledge about prospective Health Technology Assessment improves. Therefore, an adaptive approach to store the collected data is required. Not only statistical data has to be stored in a data base, but also metadata, knowledge about health care processes and simulation models should be stored in a structured way. In combination with a modular system architecture a flexible approach also helps to adapt the ProHTA system to other applications. Data mining should be utilized to improve simulation quality and find potentials for new health technologies.
In addition to the evolunionary aspect of data management, uncertainty management is an important issue for ProHTA.The uncertainty of simulation results highly depends on the uncerainty of simulation input parameters. Thus, propagating the uncertaintainty of input parameters is important in order to be able to adequately estimate the uncertainty of output parameters. In ProHTA we are also working on techniques enabling backward propagation of uncertainties. Thereby the goal is to govern data acquisition in a way that minimizes the uncertainty of the simulation output.

Project manager:
Prof. Dr. Richard Lenz

Project participants:
Dr.-Ing. Philipp Baumgärtel

Duration: 1.12.2010 - 31.12.2015

Baumgärtel, Philipp
E-Mail: philipp.baumgaertel@fau.de
Baumgärtel, Philipp ; Lenz, Richard: Towards Data and Data Quality Management for Large Scale Healthcare Simulations. In: Conchon, Emmanuel ; Correia, Carlos ; Fred, Ana ; Gamboa, Hugo (Ed.) : Proceedings of the International Conference on Health Informatics (International Conference on Health Informatics Villamoura, Portugal 01.-04.02.2012). 2012, pp 275-280.
Baumgärtel, Philipp ; Endler, Gregor ; Held, Johannes ; Lenz, Richard: Pay-as-you-go data integration for large scale healthcare simulations. In: Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS) (Org.) : GMDS 2012 (57. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS) Braunschweig 16.-20.09.2012). Düsseldorf : German Medical Science GMS Publishing House, 2012.
Baumgärtel, Philipp ; Endler, Gregor ; Lenz, Richard: A Benchmark for Multidimensional Statistical Data. In: Catania, Barbara ; Guerrini, Giovanna ; Pokorný, Jaroslav (Ed.) : Advances in Databases and Information Systems (ADBIS 2013 Conference Genoa, Italy 01-04.09.2013). Berlin : Springer, 2013, pp 358-371. - ISBN 978-3-642-40682-9
Baumgärtel, Philipp ; Tenschert, Johannes Christian ; Lenz, Richard: A Query Language for Workflow Instance Data. In: Catania, Barbara ; Cerquitelli, Tania ; Chiusano, Silvia ; Guerrini, Giovanna ; Kämpf, Mirko ; Kemper, Alfons ; Novikov, Boris ; Palpanas, Themis ; Pokorný, Jaroslav ; Vakali, Athena (Ed.) : New Trends in Databases and Information Systems (ADBIS 2013 Conference Genoa, Italy 01-04.09.2013). Schweiz : Springer, 2014, pp 79-86. - ISBN 978-3-319-01862-1
Baumgärtel, Philipp ; Endler, Gregor ; Lenz, Richard: Toward Pay-As-You-Go Data Integration for Healthcare Simulations. In: Bienkiewicz, Marta ; Verdier, Christine ; Plantier, Guy ; Schultz, Tanja ; Fred, Ana ; Gamboa, Hugo (Ed.) : Proceedings of the International Conference on Health Informatics (International Conference on Health Informatics 2014 Loire Valley, France 03. - 06.03. 2014). Portugal : SciTePress, 2014, pp 172-177. - ISBN 978-989-758-010-9
Baumgärtel, Philipp ; Endler, Gregor ; Wahl, Andreas Maximilian ; Lenz, Richard: Inverse Uncertainty Propagation for Demand Driven Data Acquisition. In: Tolk, A. ; Diallo, S. Y. ; Ryzhov, I. O. ; Yilmaz, L. ; Buckley, S. ; Miller, J. A. (Ed.) : Proceedings of the 2014 Winter Simulation Conference (Winter Simulation Conference 2014 Savannah, GA 31421, USA 07-10.12.2014). Piscataway, NJ, USA : IEEE Press, 2014, pp 710-721.
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