Mar 22, 2019 | 16:00—17:30
The amount of medical data generated in healthcare has exponentially increased in recent years. To gain a quantitative understanding of the complex processes involved in healthcare, we need new scientific methods to adequately investigate the heterogeneous and dynamic data they produce. Data-driven analysis, when applied in a clinical context, has the aim to turn complex medical data records into knowledge on how to prevent or treat diseases more effectively. This approach requires the combination of data-related, methodological expertise with in-depth know-how of the involved clinical or medical domain. More precisely, it requires interdisciplinary collaboration, since data analysis competencies needs to be consistent with medical domain expertise. However, successful interdisciplinary research remains a great challenge. In order to transform medical data into knowledge, we need to gain new insights into factors that support interdisciplinary interaction and the emergence of cognitive processes through which disciplinary concepts and methods are integrated. Therefore, this master thesis has two objectives. First, to carry out a data-driven analysis by means of an interdisciplinary research team and, second, to combine our practical experience with theoretical knowledge about cognitive processes to gain knowledge about interdisciplinary research. In terms of methodological data analysis approaches, we applied multiple logistic regression to analyse the dosage-dependent occurrence of osteoporosis in statin patients. This study reveals that there is a highly non-trivial dependence of statin dosage with the odds of osteoporosis. To the best of our knowledge, this is the first study which shows that it is important to consider both potency and dosages when investigating the relationship of osteoporosis and statin therapy. Our results show that the diagnosis of osteoporosis is underrepresented in low-dose and overrepresented in high-dose statin treatment. The second high-level objective was to show how interdisciplinarity promotes data-driven analysis and to gain insights into factors that support the interaction process between researchers from different disciplines and the emergence of a cognitive process among them. We made use of contemporary cognitive science theories and Bourdieu’s theory of practice to get an alternative perspective on interdisciplinarity. Based on these theoretical insights combined with our own research experience, we identified body—mind–environment actors that we consider important for a successful research process: personal/habitus-related qualities, environmental/field-related properties, a shared study logic and time. In summary, this work demonstrates how data-driven analysis is promoted by interdisciplinary research.