Literature DB >> 21587298

Using electronic health records to drive discovery in disease genomics.

Isaac S Kohane1.   

Abstract

If genomic studies are to be a clinically relevant and timely reflection of the relationship between genetics and health status--whether for common or rare variants--cost-effective ways must be found to measure both the genetic variation and the phenotypic characteristics of large populations, including the comprehensive and up-to-date record of their medical treatment. The adoption of electronic health records, used by clinicians to document clinical care, is becoming widespread and recent studies demonstrate that they can be effectively employed for genetic studies using the informational and biological 'by-products' of health-care delivery while maintaining patient privacy.

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Year:  2011        PMID: 21587298     DOI: 10.1038/nrg2999

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  90 in total

1.  Analysis of identifier performance using a deterministic linkage algorithm.

Authors:  Shaun J Grannis; J Marc Overhage; Clement J McDonald
Journal:  Proc AMIA Symp       Date:  2002

2.  A vision for the future of genomics research.

Authors:  Francis S Collins; Eric D Green; Alan E Guttmacher; Mark S Guyer
Journal:  Nature       Date:  2003-04-14       Impact factor: 49.962

3.  The use of health information technology in seven nations.

Authors:  Ashish K Jha; David Doolan; Daniel Grandt; Tim Scott; David W Bates
Journal:  Int J Med Inform       Date:  2008-07-25       Impact factor: 4.046

4.  Personal genomes: when consent gets in the way.

Authors:  Patrick Taylor
Journal:  Nature       Date:  2008-11-06       Impact factor: 49.962

5.  Progress in medical information management. Systematized nomenclature of medicine (SNOMED).

Authors:  R A Côté; S Robboy
Journal:  JAMA       Date:  1980 Feb 22-29       Impact factor: 56.272

6.  Run-in periods in randomized trials: implications for the application of results in clinical practice.

Authors:  A Pablos-Méndez; R G Barr; S Shea
Journal:  JAMA       Date:  1998-01-21       Impact factor: 56.272

7.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.

Authors:  Joshua C Denny; Marylyn D Ritchie; Melissa A Basford; Jill M Pulley; Lisa Bastarache; Kristin Brown-Gentry; Deede Wang; Dan R Masys; Dan M Roden; Dana C Crawford
Journal:  Bioinformatics       Date:  2010-03-24       Impact factor: 6.937

8.  An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus.

Authors:  Chirag J Patel; Jayanta Bhattacharya; Atul J Butte
Journal:  PLoS One       Date:  2010-05-20       Impact factor: 3.240

9.  Association between fine particulate matter and diabetes prevalence in the U.S.

Authors:  John F Pearson; Chethan Bachireddy; Sangameswaran Shyamprasad; Allison B Goldfine; John S Brownstein
Journal:  Diabetes Care       Date:  2010-07-13       Impact factor: 19.112

10.  A cost-effectiveness analysis of subject recruitment strategies in the HIPAA era: results from a colorectal cancer screening adherence trial.

Authors:  Paul C Schroy; Julie T Glick; Patricia Robinson; Maria A Lydotes; Timothy C Heeren; Marianne Prout; Peter Davidson; John B Wong
Journal:  Clin Trials       Date:  2009-11-23       Impact factor: 2.486

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  164 in total

1.  Using Electronic Health Records To Generate Phenotypes For Research.

Authors:  Sarah A Pendergrass; Dana C Crawford
Journal:  Curr Protoc Hum Genet       Date:  2018-12-05

2.  A translational engine at the national scale: informatics for integrating biology and the bedside.

Authors:  Isaac S Kohane; Susanne E Churchill; Shawn N Murphy
Journal:  J Am Med Inform Assoc       Date:  2011-11-10       Impact factor: 4.497

3.  (Mis)treating the pharmacogenetic incidentalome.

Authors:  Isaac S Kohane
Journal:  Nat Rev Drug Discov       Date:  2012-02-01       Impact factor: 84.694

4.  Cardiovascular epidemiology in a changing world--challenges to investigators and the National Heart, Lung, and Blood Institute.

Authors:  Paul D Sorlie; Diane E Bild; Michael S Lauer
Journal:  Am J Epidemiol       Date:  2012-03-12       Impact factor: 4.897

Review 5.  Human genotype-phenotype databases: aims, challenges and opportunities.

Authors:  Anthony J Brookes; Peter N Robinson
Journal:  Nat Rev Genet       Date:  2015-11-10       Impact factor: 53.242

Review 6.  Unravelling the human genome-phenome relationship using phenome-wide association studies.

Authors:  William S Bush; Matthew T Oetjens; Dana C Crawford
Journal:  Nat Rev Genet       Date:  2016-02-15       Impact factor: 53.242

7.  Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis.

Authors:  Finale Doshi-Velez; Yaorong Ge; Isaac Kohane
Journal:  Pediatrics       Date:  2013-12-09       Impact factor: 7.124

Review 8.  Biobanks and personalized medicine.

Authors:  J E Olson; S J Bielinski; E Ryu; E M Winkler; P Y Takahashi; J Pathak; J R Cerhan
Journal:  Clin Genet       Date:  2014-03-27       Impact factor: 4.438

Review 9.  Introducing the Big Knowledge to Use (BK2U) challenge.

Authors:  Yehoshua Perl; James Geller; Michael Halper; Christopher Ochs; Ling Zheng; Joan Kapusnik-Uner
Journal:  Ann N Y Acad Sci       Date:  2016-10-17       Impact factor: 5.691

Review 10.  The intelligent use and clinical benefits of electronic medical records in multiple sclerosis.

Authors:  Mary F Davis; Jonathan L Haines
Journal:  Expert Rev Clin Immunol       Date:  2014-12-11       Impact factor: 4.473

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