An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Background: Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform.

Are You the Outlier? Identifying Targets for Privacy Attacks on Health Datasets.

The identification of vulnerable records (targets) is an important step for many privacy attacks on protected health data. We implemented and evaluated three outlier metrics for detecting potential targets. Next, we assessed differences and similarities between the top-k targets suggested by the different methods and studied how susceptible those targets are to membership inference attacks on synthetic data.

Health Data Re-Identification: Assessing Adversaries and Potential Harms.

Sharing biomedical data for research can help to improve disease understanding and support the development of preventive, diagnostic, and therapeutic methods. However, it is vital to balance the amount of data shared and the sharing mechanism chosen with the privacy protection provided. This requires a detailed understanding of potential adversaries who might attempt to re-identify data and the consequences of their actions.

OHDSI-compliance: a set of document templates facilitating the implementation and operation of a software stack for real-world evidence generation.

Introduction: The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations.

[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative].

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations.

Development of a Trusted Third Party at a Large University Hospital: Design and Implementation Study.

Background: Pseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring the direct identification of data to support various research processes while still allowing for advanced processing activities, such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units known as trusted third parties (TTPs).

Machine Learning for Medical Data Integration.

Making health data available for secondary use enables innovative data-driven medical research. Since modern machine learning (ML) methods and precision medicine require extensive amounts of data covering most of the standard and edge cases, it is essential to initially acquire large datasets. This can typically only be achieved by integrating different datasets from various sources and sharing data across sites.

Generating evidence on privacy outcomes to inform privacy risk management: A way forward?

Effective and efficient privacy risk management (PRM) is a necessary condition to support digitalization in health care and secondary use of patient data in research. To reduce privacy risks, current PRM frameworks are rooted in an approach trying to reduce undesired technical/organizational outcomes such as broken encryption or unintentional data disclosure. Comparing this with risk management in preventive or therapeutic medicine, a key difference becomes apparent: in health-related risk management, medicine focuses on person-specific health outcomes, whereas PRM mostly targets more indirect, technical/organizational outcomes.