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J Pathol Inform. 2015 Sep 28;6:50. doi: 10.4103/2153-3539.165999. eCollection 2015.

Practical considerations in genomic decision support: The eMERGE experience.

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, MN, 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
Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
8
Department of Biomedical Informatics, Vanderbilt University, Baltimore, MD, USA.
9
Department of Pediatrics, The Children's Hospital of Philadelphia, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
10
Group Health Research Institute, Seattle, Washington, USA.
11
Department of Biomedical Informatics, Columbia University Medical Center, New York, USA.
12
Icahn School of Medicine, Mount Sinai, New York, USA.
13
Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.
14
Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
15
Nationwide Children's Hospital, Columbus, Ohio, USA.
16
Department of Pharmacy, Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA.
17
Department of Pediatrics, University of Cincinnati College of Medicine, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
18
University of Maryland School of Medicine, Baltimore, Maryland, USA.
19
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA.
20
Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
21
Weis Center for Research, Geisinger Clinic, Danville, Pennsylvania, USA.
22
Molecular Pathology, Mashfield Labs, Marshfield, Wisconsin, USA.
23
Department of Biomedical Informatics, Columbia University, New York, USA.
24
Department of Pediatrics, Harvard Medical School, Division of Genetics and Genomics, Boston Children's Hospital, Boston, Massachusetts, USA.
25
Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA.

Abstract

BACKGROUND:

Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more.

METHODS:

In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered.

RESULTS:

Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months.

CONCLUSIONS:

These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

KEYWORDS:

Clinical decision support; genomic medicine; personalized health care; pharmacogenomics; precision medicine

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