Over the last decade, the exponential growth in patient data volume and velocity has transformed it into a valuable resource for researchers. Yet, accessing comprehensive, unique patient data sets remains a challenge, particularly when individuals have received treatments across various practices and hospitals. Traditional record linkage methods fall short in adequately protecting patient privacy in these scenarios. Privacy Preserving Record Linkage (PPRL) offers a solution, employing techniques such as data cryptographic methods to identify common patients occurring in multiple datasets, while maintaining the privacy of other patients. This paper proposes an investigation into combined approaches of two common German PPRL tools, namely E-PIX and MainSEL. Each tool, while aiming for 'privacy preservation', employs distinct methods that offer unique advantages and drawbacks. Our research aims to explore these in a combined approach to leverage their respective strengths and mitigate their limitations. We anticipate that this synergistic approach will not only enhance data privacy but also allow for easier synchronisation of research data. This study is particularly pertinent in light of evolving privacy regulations and the increasing complexity of healthcare data management. By advancing PPRL methodologies, we aim to contribute to more robust, privacy-compliant data analysis practices in healthcare research.