Learning Healthcare Systems in Pediatrics: Cross-Institutional and Data-Driven Decision-Support for Intensive Care Environments (CADDIE).

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.