This thesis examines the effects of safety notices on the utilization of knee implants in orthopedic practices. Utilizing a blend of correlation analysis, machine learning techniques, and interrupted time series analysis, this study evaluates the impact of safety notices on the market share of diverse knee implant models. The analysis leverages a dataset comprising safety notices, market share figures, and analytical model outputs to investigate both the direct and indirect influences of these notices on clinical decision-making processes.
Although safety notices are integral to regulatory compliance and patient safety under the Medical Device Regulation (MDR), which mandates continuous monitoring of device performance, findings indicate that these notices seldom significantly alter clinical practices or market shares. Notable exceptions include specific models such as Advance, Evolution, and Journey, which display variable impacts, suggesting the presence of other complex factors at play in clinical decision-making. These findings illustrate the complex dynamics of clinical responses to regulatory initiatives and underscore the need for more robust monitoring and evaluation strategies to determine the effectiveness of safety notices in post-market surveillance.
The thesis underscores the need to enhance post-market surveillance systems to more accurately assess the influence of safety notices on clinical practice, thereby addressing a crucial knowledge gap. By recommending directions for future research aimed at elucidating the subtle effects of safety notices, this work strives to maximize their utility and foster improved outcomes in patient care within the realm of medical devices. This research establishes a foundation for understanding the practical implications of safety notices in clinical environments, ensuring that regulatory measures are more closely aligned with clinical requirements, which will be useful for future research.

In treating patients with pancreatic cancer, multidisciplinary team (MDT) meetings have been established as the clinical standard for discussing patient cases using combined expertise from various specialisations. However, despite the combined expertise, 19%-33% of all pancreatic surgeries is observed to be prematurely abandoned due to locally advanced pancreatic cancer or metastatic disease. Furthermore, MDT meetings typically feature an open discussion format that can be subject to social influence factors affecting the overall objectivity of individual expert opinions. Subsequently, this research explores Artificial Swarm Intelligence (ASI) as a potential technology to overcome the aforementioned issues. Through an experiment, this research tests the use of ASI in a simulated MDT meeting and examines its effects on the accuracy of resectability assessment. Furthermore, a survey is conducted to assess the perceived impact of ASI on social loafing and social bias influences, as well as potential enablers and barriers for the potential implementation of ASI. Based on the experiment results, the use of ASI shows equal assessment accuracy compared to assessing tumor resectability through discussion as with regular MDT meetings. However, with regard to social influ- ence, participants assessed ASI to moderately drive reduction factors that reduce social loafing and social bias - suggesting indirect benefits to the objectivity of the decision process.