Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics.

Aims: Data availability remains a critical challenge in modern, data-driven medical research. Due to the sensitive nature of patient health records, they are rightfully subject to stringent privacy protection measures. One way to overcome these restrictions is to preserve patient privacy by using anonymization and synthetization strategies.

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.

Tracking provenance in clinical data warehouses for quality management.

Introduction: Data provenance, which documents the origin, history, and transformations of data, can enhance the reproducibility of processing workflows and help to address errors and quality issues. In this work, we focus on tracking and utilizing provenance information as part of quality management in Extract-Transform-Load (ETL) processes used to build clinical data warehouses.

Methods: We designed and implemented a framework that automatically tracks how data flows through an ETL process and detects errors and quality problems during processing.