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

Author:

Shirts Brian H1,Salama Joseph S2,Aronson Samuel J3,Chung Wendy K4,Gray Stacy W56,Hindorff Lucia A7,Jarvik Gail P28,Plon Sharon E9,Stoffel Elena M10,Tarczy-Hornoch Peter Z11,Van Allen Eliezer M612,Weck Karen E1314,Chute Christopher G15,Freimuth Robert R15,Grundmeier Robert W16,Hartzler Andrea L17,Li Rongling7,Peissig Peggy L18,Peterson Josh F19,Rasmussen Luke V20,Starren Justin B20,Williams Marc S21,Overby Casey L2122

Affiliation:

1. Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA

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

6. Dana-Farber Cancer Institute, Boston, MA, USA

7. National Human Genome Research Institute, NIH, Rockville, MD, USA

8. Department of Genome Sciences, University of Washington, Seattle, WA, USA

9. Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

10. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

11. Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA

12. The Broad Institute of MIT and Harvard, Cambridge, MA, USA

13. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

14. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

15. Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA

16. Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA

17. Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA

18. Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA

19. Department of Biomedical Informatics, Vanderbilt, Nashville, TN, USA

20. Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

21. Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA

22. 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

Abstract

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.

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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