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J Pathol Inform. 2015 Aug 31;6:46. doi: 10.4103/2153-3539.163985. eCollection 2015.

A conceptual model for translating omic data into clinical action.

Author information

1
Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
2
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
3
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine, Mount Sinai, New York, USA.
4
Division of Genetics and Endocrinology, Cook Children's Medical Center, Fort Worth, Texas, USA.
5
Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.
6
Division of General Internal Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
7
Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA.
8
Group Health Research Institute, Seattle, Washington, USA.
9
Icahn School of Medicine, Mount Sinai, New York, USA.
10
Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
11
Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA.
12
Nationwide Children's Hospital, Columbus, Ohio, USA.
13
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.
14
Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
15
Weis Center for Research, Danville, Pennsylvania, USA.
16
Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA.

Abstract

Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called "the omic funnel." This model parallels the classic "Data, Information, Knowledge, Wisdom pyramid" and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

KEYWORDS:

Genomic medicine; personalized health care; precision medicine

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