LAUSR

BACKGROUND The vast amount of data generated in healthcare can be reused to support decision-making by developing clinical decision-support systems. Since evidence is lacking in Pediatrics, it seems to be beneficial to design future systems towards the vision of generating evidence through cross-institutional data analysis and continuous learning cycles. OBJECTIVES Presentation of an approach for cross-institutional and data-driven decision support in pediatric intensive care units (PICU), and the long-term vision of Learning Healthcare Systems in Pediatrics. METHODS Using a four-step approach, including the design of interoperable decision-support systems and data-driven algorithms, for establishing a Learning Health Cycle. RESULTS We developed and started to follow that approach on exemplary of systemic inflammatory response syndrome (SIRS) detection in PICU. CONCLUSIONS Our approach has great potential to establish our vision of learning systems, which support decision-making in PICU by analyzing cross-institutional data and giving insights back to both, their own knowledge base and clinical care, to continuously learn about practices and evidence in Pediatrics.