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

Stud Health Technol Inform. 2018;251:109-112.

Abstract

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.

Keywords: Clinical Decision Support Systems; Critical Care; Health Information Interoperability; Learning Healthcare System; Pediatrics.

MeSH terms

  • Algorithms*
  • Child
  • Critical Care
  • Data Collection
  • Decision Support Systems, Clinical*
  • Humans
  • Intensive Care Units, Pediatric*
  • Machine Learning*
  • Pediatrics