DebugIT · Detecting and Eliminating Bacteria UsinG Information Technologies

In about half a century of antibiotic use, unexpected new challenges have come to light: fast emer-gence of resistances among pathogens, misuse and overuse of antibiotics; direct and indirect re-lated costs. Antimicrobial resistance results in escalating healthcare costs, increased morbidity and mortality and the emergence or reemergence of potentially untreatable pathogens. In this context of infectious diseases we will (1) detect patient safety issues, (2) learn how to prevent them and (3) actually prevent them in clinical cases. We will detect harmful patterns and trends using clinical and operational information from Clinical Information Systems (CIS). This will bedone through the 'view' of a virtualized Clinical Data Re-pository (CDR), featuring, transparent access to the original CIS and/or collection and aggregation of data in a local store. Text, image and structured data mining on individual patients as well as on populations will learn us informational and temporal patterns of patient harm. This knowledge will be fed into a Medical Knowledge Repository and mixed with knowledge coming from external sources (for example guidelines and evidences). After editing and validating, this knowledge will be used by adecision support and monitoring tool in the clinical environment to prevent patient safety issues and report on it.Outcomes and benefits, both clinical and economical will be measured and reported on. Innovation within this project lays in the virtualization of Clinical Data Repository through ontology mediation, the advanced mining techniques, the reasoning engine and the consolidation of all these techniques in a comprehensive but open framework. This framework will be implemented, focused on infectious diseases, but will be applicable for all sorts of clinical cases in the future.