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pSCANNER: patient-centered Scalable National Network for Effectiveness Research.
Ohno-Machado, Lucila; Agha, Zia; Bell, Douglas S; Dahm, Lisa; Day, Michele E; Doctor, Jason N; Gabriel, Davera; Kahlon, Maninder K; Kim, Katherine K; Hogarth, Michael; Matheny, Michael E; Meeker, Daniella; Nebeker, Jonathan R.
Afiliação
  • Ohno-Machado L; Division of Biomedical Informatics, Department of Medicine and Clinical Translational Research Institute, University of California San Diego, La Jolla, California, USA Division of Health Services Research & Development, Veterans Affairs, San Diego Healthcare System, La Jolla, California, USA.
  • Agha Z; Division of Health Services Research & Development, Veterans Affairs, San Diego Healthcare System, La Jolla, California, USA Division of Internal Medicine, Department of Medicine, University of California San Diego, La Jolla, California, USA.
  • Bell DS; Division of General Internal Medicine, Department of Medicine, University of California Los Angeles, Los Angeles, California, USA Department of Health, RAND Corporation, Santa Monica, California, USA.
  • Dahm L; Health Information Technology Department, Institute for Clinical and Translational Science, University of California Irvine, Orange, California, USA.
  • Day ME; Division of Biomedical Informatics, Department of Medicine and Clinical Translational Research Institute, University of California San Diego, La Jolla, California, USA.
  • Doctor JN; Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, USA.
  • Gabriel D; Center for Health and Technology, University of California Davis, Davis, California, USA.
  • Kahlon MK; Department of Neurology, School of Medicine & Clinical and Translational Science Institute, University of California San Francisco, San Francisco, California, USA.
  • Kim KK; Betty Irene Moore School of Nursing, University of California Davis, Davis, California, USA.
  • Hogarth M; Departments of Pathology and Internal Medicine, University of California Davis, Sacramento, California, USA.
  • Matheny ME; Research & Development Service, Tennessee Valley Healthcare System, Veterans Health Administration, Nashville, Tennessee, USA Division of General Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Meeker D; Department of Health, RAND Corporation, Santa Monica, California, USA.
  • Nebeker JR; VA Informatics and Computing Infrastructure (VINCI) and Geriatric Research Education and Clinical Center (GRECC), Veterans Health Administration, Salt Lake City, Utah, USA Department of Medicine, University of Utah, Salt Lake City, Utah, USA.
J Am Med Inform Assoc ; 21(4): 621-6, 2014.
Article em En | MEDLINE | ID: mdl-24780722
This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Redes de Comunicação de Computadores / Avaliação de Resultados em Cuidados de Saúde / Assistência Centrada no Paciente / Disseminação de Informação / Registros Eletrônicos de Saúde Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Redes de Comunicação de Computadores / Avaliação de Resultados em Cuidados de Saúde / Assistência Centrada no Paciente / Disseminação de Informação / Registros Eletrônicos de Saúde Idioma: En Ano de publicação: 2014 Tipo de documento: Article