Journal of Cardiovascular Translational Research

, Volume 7, Issue 8, pp 692–700 | Cite as

A Review of the Role of Electronic Health Record in Genomic Research

  • Parasuram Krishnamoorthy
  • Deepansh Gupta
  • Saurav Chatterjee
  • Jessica Huston
  • John J. Ryan


Electronic health record (EHR)-driven genomic research is a recent strategy used to answer research questions using EHR data linked to DNA samples. In models using EHR, after the subject’s DNA is collected, a linkage between the DNA sample and the EHR data is maintained. This makes the EHR the paramount source of phenotypic information. The National Human Genome Research Institute sponsored Electronic Medical Records and Genomics (eMERGE) network began in five sites in 2007 and was expanded to nine sites in 2012. This network has developed the methods and best practices for utilizing EHR as a tool for genomic research. Therefore, it is vital to understand the configuration of EHR used to capture data in clinical practice and feasibility of integration with clinical genetic test results. We present a detailed review of the role and importance of EHR in the field of genomic research.


Genomics Genome-wide association studies (GWASs) Electronic health record (EHR) Bio repository Bio bank 


  1. 1.
    Kohane, I. S. (2011). Using electronic health records to drive discovery in disease genomics. Nature Reviews. Genetics, 12, 417–428.PubMedCrossRefGoogle Scholar
  2. 2.
    Blumenthal, D. (2010). Launching HITECH. New England Journal of Medicine, 362, 382–385.PubMedCrossRefGoogle Scholar
  3. 3.
    HealthIT Dashboard (2014) Accessed 24 June 2014.
  4. 4.
    Marsolo, K., & Spooner, S. A. (2013). Clinical genomics in the world of the electronic health record. Genetics in Medicine, 15, 786–791.PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Kho, A. N., Rasmussen, L. V., Connolly, J. J., Peissig, P. L., Starren, J., et al. (2013). Practical challenges in integrating genomic data into the electronic health record. Genetics in Medicine, 15, 772–778.PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Kiyota, Y., Schneeweiss, S., Glynn, R. J., Cannuscio, C. C., Avorn, J., et al. (2004). Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. American Heart Journal, 148, 99–104.PubMedCrossRefGoogle Scholar
  7. 7.
    Dean, B. B., Lam, J., Natoli, J. L., Butler, Q., Aguilar, D., et al. (2009). Use of electronic medical records for health outcomes research: a literature review. Medical Care Research and Review, 66, 611–638.PubMedCrossRefGoogle Scholar
  8. 8.
    Elixhauser, A., Steiner, C., Harris, D. R., & Coffey, R. M. (1998). Comorbidity measures for use with administrative data. Medical Care, 36, 8–27.PubMedCrossRefGoogle Scholar
  9. 9.
    Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases, 40, 373–383.PubMedCrossRefGoogle Scholar
  10. 10.
    Li, L., Chase, H. S., Patel, C. O., Friedman, C., & Weng, C. (2008). Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study. AMIA Annual Symposium Proceedings, 6, 404–408.Google Scholar
  11. 11.
    Elkin, P. L., Ruggieri, A. P., Brown, S. H., Buntrock, J., Bauer, B. A., et al. (2001). A randomized controlled trial of the accuracy of clinical record retrieval using SNOMED-RT as compared with ICD9-CM. Proceedings AMIA Symposium, 2001, 159–163.Google Scholar
  12. 12.
    ICD-9-CM (2014). ICD9ProviderDiagnostic Codes/index.html. Accessed 24 June 2014.
  13. 13.
    ICD-10 (2014). About ICD-10. Accessed 24 June 2014.
  14. 14.
    SNOMED CT (2007). Accessed 24 June 2014.
  15. 15.
    CPT - Current Procedural Terminology (2013) physician-resources/solutions-managing-your-practice/coding-billing-insurance/ Accessed 24 June 2014.
  16. 16.
    LOINC- Logical Observation Identifiers Names and Codes (2014). Accessed 24 June 2014.
  17. 17.
    RxNorm (2014). Accessed 24 June 2014.
  18. 18.
    Huff, S. M., Rocha, R. A., McDonald, C. J., De Moor, G. J., Fiers, T., et al. (1998). Development of the logical observation identifier names and codes (LOINC) vocabulary. Journal of the American Medical Informatics Association, 5, 276–292.PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Price, M. J., Berger, P. B., Teirstein, P. S., Tanguay, J. F., Angiolillo, D. J., Investigators, G. R. A. V. I. T. A. S., et al. (2011). Standard vs high-dose clopidogrel based on platelet function testing after percutaneous coronary intervention: the GRAVITAS randomized trial. JAMA, 305, 1097–1105.PubMedCrossRefGoogle Scholar
  20. 20.
    Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18, 544–551.PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF (2008) Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform :128–144.Google Scholar
  22. 22.
    Cimino, J. J. (2013). Improving the electronic health record–are clinicians getting what they wished for? JAMA, 309, 991–992.PubMedCrossRefGoogle Scholar
  23. 23.
    Manolio, T. A. (2010). Genomewide association studies and assessment of the risk of disease. New England Journal of Medicine, 363, 166–176.PubMedCrossRefGoogle Scholar
  24. 24.
    Crosslin, D. R., McDavid, A., Weston, N., Nelson, S. C., Zheng, X., Electronic Medical RecordsGenomics (eMERGE) Network, et al. (2012). Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network. Human Genetics, 131, 639–652.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Denny, J. C., Ritchie, M. D., Crawford, D. C., Schildcrout, J. S., Ramirez, A. H., et al. (2010). Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation, 122, 2016–2021.PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Denny, J. C., Crawford, D. C., Ritchie, M. D., Bielinski, S. J., Basford, M. A., et al. (2011). Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. American Journal of Human Genetics, 89, 529–542.PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Zuvich, R. L., Armstrong, L. L., Bielinski, S. J., Bradford, Y., Carlson, C. S., et al. (2011). Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality. Genetic Epidemiology, 35, 887–898.PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    eMERGE PheKb: Phenotype Knowledge Base (2012) Accessed 10 Jan 2014.
  29. 29.
    Fullerton, S. M., Wolf, W. A., Brothers, K. B., Clayton, E. W., Crawford, D. C., et al. (2012). Return of individual research results from genome-wide association studies: experience of the electronic medical records and genomics (eMERGE) network. Genetics in Medicine, 14, 424–431.PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Kullo, I. J., Ding, K., Shameer, K., McCarty, C. A., Jarvik, G. P., et al. (2011). Complement receptor 1 gene variants are associated with erythrocyte sedimentation rate. American Journal of Human Genetics, 89, 131–138.PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Loukides, G., Denny, J. C., & Malin, B. (2010). The disclosure of diagnosis codes can breach research participants’ privacy. Journal of the American Medical Informatics Association, 17, 322–327.PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Loukides, G., Gkoulalas-Divanis, A., & Malin, B. (2010). Anonymization of electronic medical records for validating genome-wide association studies. Proceedings of the National Academy of Sciences of the United States of America, 107, 7898–7903.PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Malin, B. (2010). Secure construction of k-unlinkable patient records from distributed providers. Artificial Intelligence in Medicine, 48, 29–41.PubMedCrossRefGoogle Scholar
  34. 34.
    Malin, B., Benitez, K., & Masys, D. (2011). Never too old for anonymity: a statistical standard for demographic data sharing via the HIPAA Privacy Rule. Journal of the American Medical Informatics Association, 18, 3–10.PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    National Human Genome Research Institute eMERGE phase II pediatric RFA. (2011)–022.html. Accessed 30 May 2014.
  36. 36.
    Kullo, I. J., Jarvik, G. P., Manolio, T. A., Williams, M. S., & Roden, D. M. (2013). Leveraging the electronic health record to implement genomic medicine. Genetics in Medicine, 15, 270–271.PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Browning, S. R., & Browning, B. L. (2011). Haplotype phasing: existing methods and new developments. Nature Reviews. Genetics, 12, 703–714.PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    McCarty, C. A., Nair, A., Austin, D. M., & Giampietro, P. F. (2007). Informed consent and subject motivation to participate in a large, population-based genomics study: the Marshfield Clinic Personalized Medicine Research Project. Community Genetics, 10, 2–9.PubMedCrossRefGoogle Scholar
  39. 39.
    Roden, D. M., Pulley, J. M., Basford, M. A., Bernard, G. R., Clayton, E. W., et al. (2008). Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clinical Pharmacology and Therapeutics, 84, 362–369.PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Kho, A. N., Hayes, M. G., Rasmussen-Torvik, L., Pacheco, J. A., Thompson, W. K., et al. (2012). Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. Journal of the American Medical Informatics Association, 19, 212–218.PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Carroll, R. J., Thompson, W. K., Eyler, A. E., Mandelin, A. M., Cai, T., et al. (2012). Portability of an algorithm to identify rheumatoid arthritis in electronic health records. Journal of the American Medical Informatics Association, 19, e162–e169.PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Klein, C., Lohmann, K., & Ziegler, A. (2012). The promise and limitations of genome-wide association studies. JAMA, 308, 1867–1868. doi:10.1001/2012.jama.10823.CrossRefGoogle Scholar
  43. 43.
    Klein, C., & Ziegler, A. (2011). From GWAS to clinical utility in Parkinson’s disease. Lancet, 377, 613–614.PubMedCrossRefGoogle Scholar
  44. 44.
    Ghoussaini, M., Song, H., Koessler, T., Al Olama, A. A., Kote-Jarai, Z., et al. (2008). Multiple loci with different cancer specificities within the 8q24 gene desert. Journal of the National Cancer Institute, 100, 962–966.PubMedCentralPubMedCrossRefGoogle Scholar
  45. 45.
    Visel, A., Rubin, E. M., & Pennacchio, L. A. (2009). Genomic views of distant-acting enhancers. Nature, 461, 199–205.PubMedCentralPubMedCrossRefGoogle Scholar
  46. 46.
    Jostins, L. (2011). Barrett JC (2011) Genetic risk prediction in complex disease. Human Molecular Genetics, 20, R182–R188.PubMedCentralPubMedCrossRefGoogle Scholar
  47. 47.
    National Human Genome Research Institute. A catalog of published genome-wide association studies (2013) Accessed June 24 2014.
  48. 48.
    Nebert, D. W., Zhang, G., & Vesell, E. S. (2008). From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. Drug Metabolism Reviews, 40, 187–224.PubMedCentralPubMedCrossRefGoogle Scholar
  49. 49.
    Gymrek, M., McGuire, A. L., Golan, D., Halperin, E., & Erlich, Y. (2013). Identifying personal genomes by surname inference. Science, 339, 321–324.PubMedCrossRefGoogle Scholar
  50. 50.
    Gottesman, O., Kuivaniemi, H., Tromp, G., Faucett, W. A., Li, R., et al. (2013). (2013) The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genetics in Medicine, 15, 761–771.PubMedCentralPubMedCrossRefGoogle Scholar
  51. 51.
    Phase II Network Biorepositories, EMR Characteristics and Study samples: Electronic Medical Records and Genomics (eMERGE) Network (2014) Accessed 14 July 2014.

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Parasuram Krishnamoorthy
    • 1
  • Deepansh Gupta
    • 2
  • Saurav Chatterjee
    • 2
  • Jessica Huston
    • 3
  • John J. Ryan
    • 3
  1. 1.Department of MedicineEnglewood Hospital and Medical CenterEnglewoodUSA
  2. 2.Division of CardiologySt. Lukes-Roosevelt Hospital CenterNew YorkUSA
  3. 3.Division of Cardiovascular MedicineUniversity of Utah Health Science CenterSalt Lake CityUSA

Personalised recommendations