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      PEDSnet: a National Pediatric Learning Health System

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          Abstract

          A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.

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          Most cited references25

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          Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2).

          Informatics for Integrating Biology and the Bedside (i2b2) is one of seven projects sponsored by the NIH Roadmap National Centers for Biomedical Computing (http://www.ncbcs.org). Its mission is to provide clinical investigators with the tools necessary to integrate medical record and clinical research data in the genomics age, a software suite to construct and integrate the modern clinical research chart. i2b2 software may be used by an enterprise's research community to find sets of interesting patients from electronic patient medical record data, while preserving patient privacy through a query tool interface. Project-specific mini-databases ("data marts") can be created from these sets to make highly detailed data available on these specific patients to the investigators on the i2b2 platform, as reviewed and restricted by the Institutional Review Board. The current version of this software has been released into the public domain and is available at the URL: http://www.i2b2.org/software.
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            Children with medical complexity: an emerging population for clinical and research initiatives.

            Children with medical complexity (CMC) have medical fragility and intensive care needs that are not easily met by existing health care models. CMC may have a congenital or acquired multisystem disease, a severe neurologic condition with marked functional impairment, and/or technology dependence for activities of daily living. Although these children are at risk of poor health and family outcomes, there are few well-characterized clinical initiatives and research efforts devoted to improving their care. In this article, we present a definitional framework of CMC that consists of substantial family-identified service needs, characteristic chronic and severe conditions, functional limitations, and high health care use. We explore the diversity of existing care models and apply the principles of the chronic care model to address the clinical needs of CMC. Finally, we suggest a research agenda that uses a uniform definition to accurately describe the population and to evaluate outcomes from the perspectives of the child, the family, and the broader health care system.
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              Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership.

              The U.S. Food and Drug Administration (FDA) Amendments Act of 2007 mandated that the FDA develop a system for using automated health care data to identify risks of marketed drugs and other medical products. The Observational Medical Outcomes Partnership is a public-private partnership among the FDA, academia, data owners, and the pharmaceutical industry that is responding to the need to advance the science of active medical product safety surveillance by using existing observational databases. The Observational Medical Outcomes Partnership's transparent, open innovation approach is designed to systematically and empirically study critical governance, data resource, and methodological issues and their interrelationships in establishing a viable national program of active drug safety surveillance by using observational data. This article describes the governance structure, data-access model, methods-testing approach, and technology development of this effort, as well as the work that has been initiated.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                amiajnl
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1067-5027
                1527-974X
                July 2014
                12 May 2014
                12 May 2014
                : 21
                : 4
                : 602-606
                Affiliations
                [1 ]Department of Pediatrics, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA
                [2 ]Leonard Davis Institute of Health Economics, University of Pennsylvania , Philadelphia, Pennsylvania, USA
                [3 ]Department of Pediatrics, The Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center , University of Cincinnati College of Medicine , Cincinnati, Ohio, USA
                [4 ]Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine , Cincinnati, Ohio, USA
                [5 ]Seattle Children's Hospital , Seattle, Washington, USA
                [6 ]Department of Pediatrics, University of Washington School of Medicine , Seattle, Washington, USA
                [7 ]Division of General Pediatrics, Boston Children's Hospital , Boston, Massachusetts, USA
                [8 ]Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute , Boston, Massachusetts, USA
                [9 ]Nemours Children's Hospital and the College of Medicine, University of Central Florida , Orlando, Florida, USA
                [10 ]Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital and Departments of Pediatrics and Statistics, The Ohio State University, Columbus, Ohio, USA
                [11 ]Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia and Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA
                [12 ]Department of Pediatrics, St. Louis Children's Hospital, Washington University School of Medicine , St. Louis, Missouri, USA
                [13 ]Department of Pediatrics, Children's Hospital Colorado, University of Colorado and the Colorado Clinical and Translational Sciences Institute , Aurora, Colorado, USA
                Author notes
                [Correspondence to ] Dr Michael G. Kahn, University of Colorado, Building 500, Mail Stop 563, 13001 East 17th Place Room C1009, Aurora, CO 80045, USA; Michael.Kahn@ 123456ucdenver.edu
                Article
                amiajnl-2014-002743
                10.1136/amiajnl-2014-002743
                4078288
                24821737
                a99ec79b-dca3-4e37-b8ef-302a2a47916f
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/

                History
                : 21 February 2014
                : 9 March 2014
                Categories
                1506
                Focus on Building a Network for Patient-Centered Outcomes Research
                Brief communication
                Custom metadata
                unlocked

                Bioinformatics & Computational biology
                comparative effectivness research,distributed databases,data sharing,inflammatory bowel diseases,hypoplastic left heart syndrome,obesity

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