Else Kroener Fresenius Center for Digital Health, Faculty of Medicine Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.
Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Dresden University of Technology, Dresden, Germany.
Stud Health Technol Inform. 2024 Aug 22;316:43-47. doi: 10.3233/SHTI240340.
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.
在过去的十年中，患者数据量和速度的指数级增长使其成为研究人员的宝贵资源。然而，获取全面、独特的患者数据集仍然是一个挑战，特别是当个人在不同的实践和医院接受治疗时。在这些情况下，传统的记录链接方法在充分保护患者隐私方面存在不足。隐私保护记录链接 (PPRL) 提供了一种解决方案，它采用数据加密方法等技术来识别多个数据集中共有的常见患者，同时保护其他患者的隐私。本文提出了对两种常见的德国 PPRL 工具（即 E-PIX 和 MainSEL）的联合方法的研究。每个工具都旨在实现“隐私保护”，但采用的方法不同，各有优缺点。我们的研究旨在探索这些工具的联合使用方法，以利用它们各自的优势并减轻其局限性。我们预计，这种协同方法不仅将增强数据隐私性，还将使研究数据的同步变得更加容易。考虑到不断发展的隐私法规和医疗保健数据管理的日益复杂性，这项研究尤其重要。通过推进 PPRL 方法学，我们旨在为医疗保健研究中的更强大、符合隐私法规的数据分析实践做出贡献。
