Pieter Van Gorp
My aim is to put citizens in charge of their personal health data.
Pieter Van Gorp is an assistant professor at the School of Industrial Engineering at Eindhoven University of Technology (TU/e). He works at the Information Systems lab, with a primary focus on digital health tools. Pieter conducts research on personal health data as an economic asset. He considers health records as well as evidence-based workflow and decision support models as examples of this asset with underused economic potential. In the area of health records, he has performed research on MyPHRMachines, a platform for securely analyzing Personal Health Records in the cloud. In the area of workflow models, he has contributed novel transformations for UML, BPMN and Petri-Net models. Van Gorp is also a facilitator of reproducible research (e.g. via the SHARE20.eu cloud.)
Pieter obtained his PhD degree in Software Engineering from the University of Antwerp, where he also held a postdoc position. In addition to his work as Assistant Professor at TU/e, he is also program manager of the Data Science Center Eindhoven (DSC/e), with a primary focus on matching TU/e data science research with societal challenges and industrial needs. Pieter also holds a part-time appointment at Utrecht University of Applied Sciences with the aim of realizing societal breakthroughs via connected health games. He also regularly gives talks to ‘non-scientific’ audiences, on topics such as personal health data and gamification.
Unified health gamification can significantly improve well-being in corporate environments39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017) (2017)
Intelligent dynamic clinical checklists improved checklist compliance in the intensive care unitBritish Journal of Anaesthesia (2017)
Supporting the internet-based evaluation of research software with cloud infrastructureSoftware and Systems Modeling (2012)
Synthesizing data-centric models from business process modelsComputing (2016)
- Algorithmic Programming for Operations Management
No ancillary activities