CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Brian H Shirts¹, Joseph S Salama², Samuel J Aronson³, Wendy K Chung⁴, Stacy W Gray⁵, Lucia A Hindorff⁶, Gail P Jarvik⁷, Sharon E Plon⁸, Elena M Stoffel⁹, Peter Z Tarczy-Hornoch¹⁰, Eliezer M Van Allen¹¹, Karen E Weck¹², Christopher G Chute¹³, Robert R Freimuth¹³, Robert W Grundmeier¹⁴, Andrea L Hartzler¹⁵, Rongling Li⁶, Peggy L Peissig¹⁶, Josh F Peterson¹⁷, Luke V Rasmussen¹⁸, Justin B Starren¹⁸, Marc S Williams¹⁹, Casey L Overby²⁰

Affiliations

¹ Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA shirtsb@uw.edu.
² Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA.
³ Personalized Medicine, Partners Healthcare, Boston, MA, USA.
⁴ Department of Pediatrics, Columbia University Medical Center, New York, NY, USA.
⁵ Department of Medicine, Harvard Medical School, Boston, MA, USA Dana-Farber Cancer Institute, Boston, MA, USA.
⁶ National Human Genome Research Institute, NIH, Rockville, MD, USA.
⁷ Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA.
⁸ Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
⁹ Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
¹⁰ Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
¹¹ Dana-Farber Cancer Institute, Boston, MA, USA The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹² Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹³ Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
¹⁴ Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹⁵ Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
¹⁶ Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA.
¹⁷ Department of Biomedical Informatics, Vanderbilt, Nashville, TN, USA.
¹⁸ Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
¹⁹ Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA.
²⁰ Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA Department of Medicine, Program for Personalized and Genomic Medicine and Center for Health-Related Informatics and Bioimaging, University of Maryland School of Medicine, Baltimore, MD, USA.

PMID: 26142422
PMCID: PMC5009914
DOI: 10.1093/jamia/ocv065

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Brian H Shirts et al. J Am Med Inform Assoc. 2015 Nov.

. 2015 Nov;22(6):1231-42.

doi: 10.1093/jamia/ocv065. Epub 2015 Jul 3.

Authors

Affiliations

¹ Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA shirtsb@uw.edu.
² Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA.
³ Personalized Medicine, Partners Healthcare, Boston, MA, USA.
⁴ Department of Pediatrics, Columbia University Medical Center, New York, NY, USA.
⁵ Department of Medicine, Harvard Medical School, Boston, MA, USA Dana-Farber Cancer Institute, Boston, MA, USA.
⁶ National Human Genome Research Institute, NIH, Rockville, MD, USA.
⁷ Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA.
⁸ Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
⁹ Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
¹⁰ Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
¹¹ Dana-Farber Cancer Institute, Boston, MA, USA The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
¹² Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹³ Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
¹⁴ Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
¹⁵ Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA.
¹⁶ Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA.
¹⁷ Department of Biomedical Informatics, Vanderbilt, Nashville, TN, USA.
¹⁸ Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
¹⁹ Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA.
²⁰ Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA Department of Medicine, Program for Personalized and Genomic Medicine and Center for Health-Related Informatics and Bioimaging, University of Maryland School of Medicine, Baltimore, MD, USA.

PMID: 26142422
PMCID: PMC5009914
DOI: 10.1093/jamia/ocv065

Abstract

Objective: Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS).

Materials and methods: The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement.

Results: There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information.

Conclusion: Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.

Keywords: clinical decision support; electronic health records; genetics; survey; translational research.

Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.

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References

1. Hsiao C-J, Hing E. Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001–2013. NCHS data brief, no 143. Hyattsville, MD: National Center for Health Statistics; 2014. - PubMed
1. Lautenbach DM, Christensen KD, Sparks JA, Green RC. Communicating genetic risk information for common disorders in the era of genomic medicine. Ann Rev Genomics Hum Genet. 2013;14:491–513. - PMC - PubMed
1. Lynch J, Whatley A, Uchendu US, Ibrahim SA. Race and genomics in the Veterans Health Administration. Am J Public Health. 2014;104 (Suppl 4):S522–S5224. - PMC - PubMed
1. Kannry JL, Williams MS. The undiscovered country: the future of integrating genomic information into the EHR. Genet Med. 2013;15(10):842–845. - PMC - PubMed
1. Kannry JL, Williams MS. Integration of genomics into the electronic health record: mapping terra incognita. Genet Med. 2013;15(10):757–760. - PMC - PubMed

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CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Affiliations

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Authors

Affiliations

Abstract

References

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