Establishing a Cystic Fibrosis Learning Network: Interventions to promote collaboration and data-driven improvement at scale.

Introduction: A learning health network is a type of learning health system in which stakeholders use network organization to improve health and health care. Building on existing resources in the cystic fibrosis (CF) community, the Cystic Fibrosis Learning Network (CFLN) was designed to improve medical outcomes and quality of life through an intentional focus on achieving reliable evidence-based chronic care delivery and creating a system for data-driven collaborative learning.

Methods: We describe the development and growth of the CFLN considering six domains of a Network Maturity Grid: system leadership; governance and policy management; quality improvement (QI); engagement and community building; data and analytics; and research.

Streptococcal pneumonia meningitis as an initial presentation of X-linked agammaglobulinemia: A case report and discussion.

X-linked agammaglobulinemia (XLA) is a primary immunodeficiency caused by mutations in the gene for Bruton's tyrosine kinase , with affected males most commonly presenting with recurrent bacterial infections during the first few years of life. Here we present a 17-month-old male with a chief complaint of worsening rash and fever, whose history of streptococcal pneumonia meningitis at 5 months of age prompted suspicion for an underlying immunodeficiency and subsequent diagnosis of XLA. Bacterial meningitis is a rare initial presentation of XLA, and therefore physicians may easily overlook any underlying immunodeficiency.

Development and evaluation of an EHR-based computable phenotype for identification of pediatric Crohn's disease patients in a National Pediatric Learning Health System.

Objectives: To develop and evaluate the classification accuracy of a computable phenotype for pediatric Crohn's disease using electronic health record data from PEDSnet, a large, multi-institutional research network and Learning Health System.

Study Design: Using clinician and informatician input, algorithms were developed using combinations of diagnostic and medication data drawn from the PEDSnet clinical dataset which is comprised of 5.6 million children from eight U.

Organoid Medicine in Hepatology.

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