A conceptual model for translating omic data into clinical action

Timothy M Herr; Suzette J Bielinski; Erwin Bottinger; Ariel Brautbar; Murray Brilliant; Christopher G Chute; Joshua Denny; Robert R Freimuth; Andrea Hartzler; Joseph Kannry; Isaac S Kohane; Iftikhar J Kullo; Simon Lin; Jyotishman Pathak; Peggy Peissig; Jill Pulley; James Ralston; Luke Rasmussen; Dan Roden; Gerard Tromp; Marc S Williams; Justin Starren

doi:10.4103/2153-3539.163985

A conceptual model for translating omic data into clinical action

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

Authors

Timothy M Herr¹, Suzette J Bielinski², Erwin Bottinger³, Ariel Brautbar⁴, Murray Brilliant⁵, Christopher G Chute⁶, Joshua Denny⁷, Robert R Freimuth², Andrea Hartzler⁸, Joseph Kannry⁹, Isaac S Kohane¹⁰, Iftikhar J Kullo¹¹, Simon Lin¹², Jyotishman Pathak², Peggy Peissig¹³, Jill Pulley¹⁴, James Ralston⁸, Luke Rasmussen¹, Dan Roden¹⁴, Gerard Tromp¹⁵, Marc S Williams¹⁶, Justin Starren¹

Affiliations

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.

Abstract

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