Reuse of design pattern measurements for health data

Abstract

Research using health data is challenged by its heterogeneous nature, description and storage. The COVID-19 outbreak made clear that rapid analysis of observations such as clinical measurements across a large number of healthcare providers can have enormous health benefits. This has brought into focus the need for a common model of quantitative health data that enables data exchange and federated computational analysis. The application of ontologies, Semantic Web technologies and the FAIR principles is an approach used by different life science research projects, such as the European Joint Programme on Rare Diseases, to make data and metadata machine readable and thereby reduce the barriers for data sharing and analytics and harness health data for discovery. Here, we show the reuse of a pattern for measurements to model diverse health data, to demonstrate and raise visibility of the usefulness of this pattern for biomedical research.