2021, Grimberg, Frank, Asprion, Petra, Schneider, Bettina, Miho, Enkelejda, Babrak, Lmar, Habbabeh, Ali

Background: The life science industry has a strong interest in real-world data (RWD), a term that is currently being used in many ways and with varying definitions depending on the source. In this review article, we provide a summary overview of the challenges and risks regarding the use of RWD and its translation into real-world evidence and provide a classification and visualization of RWD challenges by means of the RWD Challenges Radar. Summary: Based on a systematic literature search, we identified 3 types of challenges – organizational, technological, and people-based – that must be addressed when deriving evidence from RWD to be used in drug approval and other applications. It further demonstrates that numerous different aspects, for example, related to the application field and the associated industry, must be considered. A key finding in our review is that the regulatory landscape must be carefully assessed before utilizing RWD. Key Messages: Establishing awareness and insight into the challenges and risks regarding the use of RWD will be key to taking full advantage of the RWD potential. As a result of this review, an “RWD Challenges Radar” will support the establishment of awareness by providing a comprehensive overview of the relevant aspects to be considered when employing RWD.

2019, Grimberg, Frank, Asprion, Petra, Schneider, Bettina, Miho, Enkelejda, Babrak, Lmar, Habbabeh, Ali

In this research report of the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), a classification of ‘Real World Data’ into the research landscape takes place. In addition, an identification of the still open research questions is done based on the fundamental principles and properties. The manifold potential of this relatively new data set is illustrated by a presentation of the already existing but also conceivable future application possibilities. Finally, the contribution of the FHNW, based on its specific competencies, to the further application of the dataset is shown.

2020, Schneider, Bettina, Bontempo Salgueiro, Nathalia, Asprion, Petra, Habbabeh, Ali

2019, Habbabeh, Ali, Schneider, Bettina, Asprion, Petra

The European General Data Protection Regulation (GDPR), which has become applicable in May 2018, obliges companies and thus Higher Education Institutions (HEIs) to (re)assess their data privacy procedures in particular the processing of personal data. As the new law unfolds an extraterritorial scope, HEIs located outside the European Union (EU) also need to examine whether they are affected, and, if so, take the necessary measures. There is a lack of discussion and approaches in the current literature as to how HEIs can comply with the GDPR regulations. The aim of this study is therefore to analyse scientific publications in order to deliver two results: Firstly, consolidated relevant recommendations and requirements in the context of GDPR, and, secondly, an instrument to help HEIs to raise their GDPR awareness. The latter was built by applying design science guidelines and resulted on a whole of 44 controls that yield a total score. The resulting value can serve as an indicator of a HEI's accordance with GDPR regulations. In addition, the compiled controls can be used as a management instrument to assess the measures taken and to continuously promote compliance with GDPR.