Literature DB >> 25006141

Rapid learning: a breakthrough agenda.

Lynn M Etheredge1.   

Abstract

A "rapid-learning health system" was proposed in a 2007 thematic issue of Health Affairs. The system was envisioned as one that uses evidence-based medicine to quickly determine the best possible treatments for patients. It does so by drawing on electronic health records and the power of big data to access large volumes of information from a variety of sources at high speed. The foundation for a rapid-learning health system was laid during 2007-13 by workshops, policy papers, large public investments in databases and research programs, and developing learning systems. Challenges now include implementing a new clinical research system with several hundred million patients, modernizing clinical trials and registries, devising and funding research on national priorities, and analyzing genetic and other factors that influence diseases and responses to treatment. Next steps also should aim to improve comparative effectiveness research; build on investments in health information technology to standardize handling of genetic information and support information exchange through apps and software modules; and develop new tools, data, and information for clinical decision support. Further advances will require commitment, leadership, and public-private and global collaboration. Project HOPE—The People-to-People Health Foundation, Inc.

Entities:  

Keywords:  Evidence-Based Medicine; Information Technology; Public Health; Quality Of Care; Research And Technology

Mesh:

Year:  2014        PMID: 25006141     DOI: 10.1377/hlthaff.2014.0043

Source DB:  PubMed          Journal:  Health Aff (Millwood)        ISSN: 0278-2715            Impact factor:   6.301


  25 in total

1.  Specification Issues in a Big Data Context: Controlling for the Endogeneity of Consumer and Provider Behaviours in Healthcare Treatment Effects Models.

Authors:  William H Crown
Journal:  Pharmacoeconomics       Date:  2016-02       Impact factor: 4.981

2.  A Growing Consensus for Change in Interpretation of Clinical Research Evidence.

Authors:  Gary B Wilkerson; Craig R Denegar
Journal:  J Athl Train       Date:  2018-03       Impact factor: 2.860

3.  Characterizing treatment pathways at scale using the OHDSI network.

Authors:  George Hripcsak; Patrick B Ryan; Jon D Duke; Nigam H Shah; Rae Woong Park; Vojtech Huser; Marc A Suchard; Martijn J Schuemie; Frank J DeFalco; Adler Perotte; Juan M Banda; Christian G Reich; Lisa M Schilling; Michael E Matheny; Daniella Meeker; Nicole Pratt; David Madigan
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-06       Impact factor: 11.205

4.  Building bridges across electronic health record systems through inferred phenotypic topics.

Authors:  You Chen; Joydeep Ghosh; Cosmin Adrian Bejan; Carl A Gunter; Siddharth Gupta; Abel Kho; David Liebovitz; Jimeng Sun; Joshua Denny; Bradley Malin
Journal:  J Biomed Inform       Date:  2015-04-01       Impact factor: 6.317

5.  Toward an Information Infrastructure for Global Health Improvement.

Authors:  C P Friedman; J C Rubin; K J Sullivan
Journal:  Yearb Med Inform       Date:  2017-09-11

6.  Improving the Odds of Success for Precision Medicine Using the Social Ecological Model.

Authors:  Scott P McGrath
Journal:  AMIA Annu Symp Proc       Date:  2020-03-04

7.  Data that drive: Closing the loop in the learning hospital system.

Authors:  Vincent X Liu; John W Morehouse; Jennifer M Baker; John D Greene; Patricia Kipnis; Gabriel J Escobar
Journal:  J Hosp Med       Date:  2016-11       Impact factor: 2.960

8.  How Dissemination and Implementation Science Can Contribute to the Advancement of Learning Health Systems.

Authors:  Katy E Trinkley; P Michael Ho; Russell E Glasgow; Amy G Huebschmann
Journal:  Acad Med       Date:  2022-09-23       Impact factor: 7.840

9.  Unsupervised characterization of Major Depressive Disorder medication treatment pathways.

Authors:  Barrett Jones; Colin G Walsh
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

10.  Big data in oncologic imaging.

Authors:  Daniele Regge; Simone Mazzetti; Valentina Giannini; Christian Bracco; Michele Stasi
Journal:  Radiol Med       Date:  2016-09-13       Impact factor: 3.469

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