2023, Asprion, Petra, Grieder, Hermann, Grimberg, Frank

Organizations today need internal reporting channels to report illegal/unethical misconduct. For this purpose, organizations set up one or more - often digital - internal reporting channels. Persons/Employees who want to report misconduct, so-called whistleblowers, expose themselves to reprisals and therefore need trustworthy reporting channels which ensure ´Digital Trust´. Blockchain, a technology that overcomes the need for trust due to its properties of immutability and integrity of data, could be promising as underlying technology for a digital reporting channel which is recognized as trustworthy. In our research, we explored multiple perspectives relevant to a trustworthy digital reporting system. Applying design science research, we evaluated the current state of the art of (digital) reporting channels and developed a prototypical blockchain-based reporting solution called “Integrity@Inside”. The prototype is being iteratively demonstrated and pre-evaluated.

2022, Asprion, Petra, Grieder, Hermann, Grimberg, Frank

Integrity@Inside leverages blockchain technology to prototype a web-based internal reporting channel that is compliant with the new EU Directive 2019/1937.

2019, Schneider, Bettina, Asprion, Petra, Grimberg, Frank, Filipe, Joaquim, Smialek, Michal, Brodsky, Alexander, Hammoudi, Slimane

This study indicates the significance of a human-centered perspective in the analysis and interpretation of Real World Data. As an exemplary use-case, the construct of perceived ‘Health-related Quality of Life’ is chosen to show, firstly, the significance of Real World Data and, secondly, the associated ‘Real World Evidence’. We settled on an iterative methodology and used hermeneutics for a detailed literature analysis to outline the relevance and the need for a forward-thinking approach to deal with Real World Evidence in the life science and health care industry. The novelty of the study is its focus on a human-centered artificial intelligence, which can be achieved by using ‘System Dynamics’ modelling techniques. The outcome – a human-centered ‘Indicator Set’ can be combined with results from data-driven, AI-based analytics. With this multidimensional approach, human intelligence and artificial intelligence can be intertwined towards an enriched Real World Evidence. The developed approach considers three perspectives – the elementary, the algorithmic and – as novelty – the human-centered evidence. As conclusion, we claim that Real World Data are more valuable and applicable to achieve patient-centricity and personalization if the human-centered perspective is considered ‘by design’.

2022-10-18, Degen, Markus, Babrak, Lmar, Smakaj, Erand, Agac, Teyfik, Asprion, Petra, Grimberg, Frank, Van der Werf, Daan, Van Ginkel, Erwin Willem, Tosoni, Deniz David, Clay, Ieuan, Brodbeck, Dominique, Natali, Eriberto, Schkommodau, Erik, Miho, Enkelejda

Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence.

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.

2022, Babrak, Lmar, Smakaj, Erand, Agac, Teyfik, Asprion, Petra, Grimberg, Frank, Van der Werf, Daan, van Ginkel, Erwin Willem, Tosoni, Deniz David, Clay, Ieuan, Degen, Markus, Brodbeck, Dominique, Natali, Eriberto Noel, Schkommodau, Erik, Miho, Enkelejda

Background: Digital technologies are transforming the health care system. A large part of information is generated as real-world data (RWD). Data from electronic health records and digital biomarkers have the potential to reveal associations between the benefits and adverse events of medicines, establish new patient-stratification principles, expose unknown disease correlations, and inform on preventive measures. The impact for health care payers and providers, the biopharmaceutical industry, and governments is massive in terms of health outcomes, quality of care, and cost. However, a framework to assess the preliminary quality of RWD is missing, thus hindering the conduct of population-based observational studies to support regulatory decision-making and real-world evidence. Objective: To address the need to qualify RWD, we aimed to build a web application as a tool to translate characterization of some quality parameters of RWD into a metric and propose a standard framework for evaluating the quality of the RWD. Methods: The RWD-Cockpit systematically scores data sets based on proposed quality metrics and customizable variables chosen by the user. Sleep RWD generated de novo and publicly available data sets were used to validate the usability and applicability of the web application. The RWD quality score is based on the evaluation of 7 variables: manageability specifies access and publication status; complexity defines univariate, multivariate, and longitudinal data; sample size indicates the size of the sample or samples; privacy and liability stipulates privacy rules; accessibility specifies how the data set can be accessed and to what granularity; periodicity specifies how often the data set is updated; and standardization specifies whether the data set adheres to any specific technical or metadata standard. These variables are associated with several descriptors that define specific characteristics of the data set. Results: To address the need to qualify RWD, we built the RWD-Cockpit web application, which proposes a framework and applies a common standard for a preliminary evaluation of RWD quality across data sets—molecular, phenotypical, and social—and proposes a standard that can be further personalized by the community retaining an internal standard. Applied to 2 different case studies—de novo–generated sleep data and publicly available data sets—the RWD-Cockpit could identify and provide researchers with variables that might increase quality Conclusions: The results from the application of the framework of RWD metrics implemented in the RWD-Cockpit application suggests that multiple data sets can be preliminarily evaluated in terms of quality using the proposed metrics. The output scores—quality identifiers—provide a first quality assessment for the use of RWD. Although extensive challenges remain to be addressed to set RWD quality standards, our proposal can serve as an initial blueprint for community efforts in the characterization of RWD quality for regulated settings.

2020, Asprion, Petra, Schneider, Bettina, Moriggl, Pascal, Grimberg, Frank, Dermol, Valerij

Lego Serious Play (LSP) is a methodology that helps people brainstorm and discuss complex ideas through storytelling and metaphors. LSP has been successfully applied as a mechanism for creative learning and team building. In this paper, we discuss using LSP to teach core topics of Cyber Security and Resilience (CS&R) in higher education. Initial results suggest that LSP has a positive impact on student learning, while also improving student engagement both, within the course and in their business environment. While the use of LSP discussed here focuses on its implementation in CS&R courses, this highly transferable methodology can be applied across the spectrum of disciplines and for multiple purposes. In addition, it can also be used to facilitate cyber security awareness or risk assessment workshops in various environments.

2018, Schneider, Bettina, Asprion, Petra, Grimberg, Frank, Shi, Yong, Fu, Haohuan, Tian, Yingjie, Krzhizhanovskaya, Valeria V., Lees, Michael Harold, Dongarra, Jack, Sloot, Peter M. A.

Enterprise Resource Planning Systems (ERPS) have continually extended their scope over the last decades. The evolution has currently reached a stage where ERPS support the entire value chain of an enterprise. This study deals with the rise of a new era, where ERPS is transformed into so-called interconnected Enterprise Systems (iES), which have a strong outside-orientation and provide a networked ecosystem open to human and technological actors (e.g. social media, Internet of Things). Higher education institutions need to prepare their students to understand the shift and to transfer the implications to today’s business world. Based on literature and applied learning scenarios the study shows existing approaches to the use of ERPS in teaching and elaborates whether and how they can still be used. In addition, implications are outlined and the necessary changes towards new teaching approaches for iES are proposed.