Semantic Interoperability for Integration of Clinical Information and Quality Indicators

Quality indicators are tools to measure the quality of healthcare. To ensure the validity and comparability of obtained results and to save time, clinical quality indicators should ideally be derived automatically based on previously collected clinical data. Main problems on the path towards automated derivation are the lack of semantic interoperability between quality indicators and clinical data, as well as between heterogeneous clinical data sources, and insufficient data quality. To reach semantic interoperability, I will formalise quality indicators and represent them as well as clinical data in a suitable format in order to apply quality indicators in practice. The novelty of the expected results lies mainly in a method for standardisation and formalisation of quality indicators and a generic approach to automatically calculate quality indicators. Regarding the problem of insufficient data quality, I will contribute data-driven feedback methods to increase the quality of data and awareness on the current performance regarding quality indicators during data entry. The designed methods will be tested in a clinical environment to evaluate their feasibility in practice.