Strengthening Rare Disease Data Readiness: a semantic approach to cross-registry interoperability

Abstract

Rare diseases affect an estimated 446 million people worldwide, yet reliable epidemiological data are scarce due to insufficient data sources and lack of standardization. The German National Registry for Rare Diseases (NARSE) aims to establish a comprehensive epidemiological data basis and enable data availability for research. However, limited interoperability between clinical and epidemiological data sources restricts secondary use and cross-registry research. This study assesses the semantic interoperability between NARSE and two core data sets: the German Medical Informatics Initiative (MII) core data set and the European Rare Disease Registry Infrastructure (ERDRI) core data set. Using a formal consensus process, mapping categories (exactMatch, closeMatch, broadMatch, narrowMatch, relatedMatch) were defined, analyzed, and evaluated for information loss using a heuristic scale. Most NARSE elements aligned with the MII core data set, while mapping to the ERDRI core data set revealed more challenges due to undefined value sets and inconsistent coding systems. These findings highlight the importance of consistent application of coding standards to safeguard information value and improve interoperability in rare disease research.