Skip to main content

Advertisement

Log in

The Electronic Health Record for Translational Research

  • Published:
Journal of Cardiovascular Translational Research Aims and scope Submit manuscript

Abstract

With growing adoption and use, the electronic health record (EHR) represents a rich source of clinical data that also offers many benefits for secondary use in biomedical research. Such benefits include access to a more comprehensive medical history, cost reductions, and increased efficiency in conducting research, as well as opportunities to evaluate new and expanded populations for sufficient statistical power. Existing work utilizing EHR data has uncovered some complexities and considerations for their use but, more importantly, has also generated practical lessons and solutions. Given an understanding of EHR data use in cardiovascular research, expanded adoption of this data source offers great potential to further transform the research landscape.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

CPT:

Current procedural terminology

EHR:

Electronic health record

eMERGE:

Electronic Medical Records and Genomics

GWAS:

Genome-wide association study

ICD:

International Classification of Diseases

NLP:

Natural language processing

QDM:

Quality Data Model

SHARP:

Strategic Health IT Research Program

SVM:

Support vector machine

References

  1. Hsiao, C.-J., & Hing, E. (2014). Use and characteristics of electronic health record systems among office-based physician practices: United States, 2001–2013. NCHS data brief, no 143. Hyattsville: National Center for Health Statistics.

    Google Scholar 

  2. Gottesman, O., Kuivaniemi, H., Tromp, G., Faucett, W. A., Li, R., Manolio, T. A., Sanderson, S. C., Kannry, J., Zinberg, R., Basford, M. A., Brilliant, M., Carey, D. J., Chisholm, R. L., Chute, C. G., Connolly, J. J., Crosslin, D., Denny, J. C., Gallego, C. J., Haines, J. L., Hakonarson, H., Harley, J., Jarvik, G. P., Kohane, I., Kullo, I. J., Larson, E. B., McCarty, C., Ritchie, M. D., Roden, D. M., Smith, M. E., Bottinger, E. P., & Williams, M. S. (2013). The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Medicine: Official Journal of the American College of Medical Genetics, 15(10), 761–771. doi:10.1038/gim.2013.72.

    Article  Google Scholar 

  3. Phenotype Knowledgebase (PheKB) (2014) http://www.phekb.org/. Accessed 6 May 2014

  4. Pathak, J., Bailey, K. R., Beebe, C. E., Bethard, S., Carrell, D. C., Chen, P. J., Dligach, D., Endle, C. M., Hart, L. A., Haug, P. J., Huff, S. M., Kaggal, V. C., Li, D., Liu, H., Marchant, K., Masanz, J., Miller, T., Oniki, T. A., Palmer, M., Peterson, K. J., Rea, S., Savova, G. K., Stancl, C. R., Sohn, S., Solbrig, H. R., Suesse, D. B., Tao, C., Taylor, D. P., Westberg, L., Wu, S., Zhuo, N., & Chute, C. G. (2013). Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. Journal of the American Medical Informatics Association: JAMIA, 20(e2), e341–348. doi:10.1136/amiajnl-2013-001939.

    Article  PubMed  Google Scholar 

  5. El Fadly, A., Rance, B., Lucas, N., Mead, C., Chatellier, G., Lastic, P. Y., Jaulent, M. C., & Daniel, C. (2011). Integrating clinical research with the Healthcare Enterprise: from the RE-USE project to the EHR4CR platform. Journal of Biomedical Informatics, 44(Suppl 1), S94–102. doi:10.1016/j.jbi.2011.07.007.

    Article  PubMed  Google Scholar 

  6. EHR4CR (2014) EHR4CR: Electronic Health Records for Clinical Research. http://www.ehr4cr.eu/. Accessed 30 May 2014

  7. Bowton, E., Field, J. R., Wang, S., Schildcrout, J. S., Van Driest, S. L., Delaney, J. T., Cowan, J., Weeke, P., Mosley, J. D., Wells, Q. S., Karnes, J. H., Shaffer, C., Peterson, J. F., Denny, J. C., Roden, D. M., & Pulley, J. M. (2014). Biobanks and electronic medical records: enabling cost-effective research. Science Translational Medicine, 6(234), 234cm233. doi:10.1126/scitranslmed.3008604.

    Article  Google Scholar 

  8. Jensen, P. B., Jensen, L. J., & Brunak, S. (2012). Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6), 395–405.

    Article  CAS  PubMed  Google Scholar 

  9. Denny, J. C. (2012). Chapter 13: Mining electronic health records in the genomics era. PLoS Computational Biology, 8(12), e1002823. doi:10.1371/journal.pcbi.1002823.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Kohane, I. S. (2011). Using electronic health records to drive discovery in disease genomics. Nature Reviews Genetics, 12(6), 417–428. doi:10.1038/nrg2999.

    Article  CAS  PubMed  Google Scholar 

  11. Weiner, M. G., Lyman, J. A., Murphy, S., & Weiner, M. (2007). Electronic health records: high-quality electronic data for higher-quality clinical research. Informatics in Primary Care, 15(2), 121–127.

    PubMed  Google Scholar 

  12. Hershberger, R. E. (2008). Cardiovascular genetic medicine: evolving concepts, rationale, and implementation. Journal of Cardiovascular Translational Research, 1, 137–143.

    Article  PubMed  Google Scholar 

  13. Boyd, A.D., Li, J.J., Burton, M.D., Jonen, M., Gardeux, V., Achour, I., Luo, R.Q., Zenku, I., Bahroos, N., Brown, S.B., Vanden Hoek, T., Lussier, Y.A. (2013). The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools. Journal of the American Medical Informatics Association

  14. Shivade, C., Raghavan, P., Fosler-Lussier, E., Embi, P. J., Elhadad, N., Johnson, S. B., & Lai, A. M. (2014). A review of approaches to identifying patient phenotype cohorts using electronic health records. Journal of the American Medical Informatics Association : JAMIA, 21(2), 221–230. doi:10.1136/amiajnl-2013-001935.

    Article  PubMed Central  PubMed  Google Scholar 

  15. Thompson, W. K., Rasmussen, L. V., Pacheco, J. A., Peissig, P. L., Denny, J. C., Kho, A. N., Miller, A., & Pathak, J. (2012). An evaluation of the NQF quality data model for representing electronic health record driven phenotyping algorithms. AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium, 2012, 911–920.

    PubMed Central  PubMed  Google Scholar 

  16. Walsh, S. H. (2004). The clinician's perspective on electronic health records and how they can affect patient care. BMJ (Clinical research ed), 328(7449), 1184–1187. doi:10.1136/bmj.328.7449.1184.

    Article  Google Scholar 

  17. Fernando, B., Kalra, D., Morrison, Z., Byrne, E., & Sheikh, A. (2012). Benefits and risks of structuring and/or coding the presenting patient history in the electronic health record: systematic review. BMJ Quality & Safety. doi:10.1136/bmjqs-2011-000450.

    Google Scholar 

  18. Rasmussen, L. V., Peissig, P. L., McCarty, C. A., & Starren, J. (2012). Development of an optical character recognition pipeline for handwritten form fields from an electronic health record. Journal of the American Medical Informatics Association: JAMIA, 19(e1), e90–95. doi:10.1136/amiajnl-2011-000182.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Peissig, P. L., Rasmussen, L. V., Berg, R. L., Linneman, J. G., McCarty, C. A., Waudby, C., Chen, L., Denny, J. C., Wilke, R. A., Pathak, J., Carrell, D., Kho, A. N., & Starren, J. B. (2012). Importance of multi-modal approaches to effectively identify cataract cases from electronic health records. Journal of the American Medical Informatics Association: JAMIA, 19(2), 225–234. doi:10.1136/amiajnl-2011-000456.

    Article  PubMed Central  PubMed  Google Scholar 

  20. Denaxas, S. C., George, J., Herrett, E., Shah, A. D., Kalra, D., Hingorani, A. D., Kivimaki, M., Timmis, A. D., Smeeth, L., & Hemingway, H. (2012). Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER). International Journal of Epidemiology, 41(6), 1625–1638. doi:10.1093/ije/dys188.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Kottke, T. E., Baechler, C. J., & Parker, E. D. (2012). Accuracy of heart disease prevalence estimated from claims data compared with an electronic health record. Preventing Chronic Disease, 9, E141. doi:10.5888/pcd9.120009.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Kottke, T. E., & Baechler, C. J. (2013). An algorithm that identifies coronary and heart failure events in the electronic health record. Preventing Chronic Disease, 10, E29. doi:10.5888/pcd10.120097.

    PubMed Central  PubMed  Google Scholar 

  23. Green, B. B., Anderson, M. L., Cook, A. J., Catz, S., Fishman, P. A., McClure, J. B., & Reid, R. (2012). Using body mass index data in the electronic health record to calculate cardiovascular risk. American Journal of Preventive Medicine, 42(4), 342–347. doi:10.1016/j.amepre.2011.12.009.

    Article  PubMed Central  PubMed  Google Scholar 

  24. Dalton, A. R., Bottle, A., Soljak, M., Okoro, C., Majeed, A., & Millett, C. (2011). The comparison of cardiovascular risk scores using two methods of substituting missing risk factor data in patient medical records. Informatics in Primary Care, 19(4), 225–232.

    PubMed  Google Scholar 

  25. Takx, R. A. P., de Jong, P. A., Leiner, T., Oudkerk, M., de Koning, H. J., Mol, C. P., Viergever, M. A., & Išgum, I. (2014). Automated coronary artery calcification scoring in non-gated chest CT: agreement and reliability. PLoS ONE, 9(3), e91239. doi:10.1371/journal.pone.0091239.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Zhong, L., Zhang, J.-M., Zhao, X., Tan, R. S., & Wan, M. (2014). Automatic localization of the left ventricle from cardiac cine magnetic resonance imaging: a new spectrum-based computer-aided tool. PLoS ONE, 9(4), e92382. doi:10.1371/journal.pone.0092382.

    Article  PubMed Central  PubMed  Google Scholar 

  27. Hongzong, S., Tao, W., Xiaojun, Y., Huanxiang, L., Zhide, H., Mancang, L., & BoTao, F. (2007). Support vector machines classification for discriminating coronary heart disease patients from non-coronary heart disease. The West Indian Medical Journal, 56(5), 451–457.

    CAS  PubMed  Google Scholar 

  28. Alty, S.R., Millasseau, S.C., Chowienczyc, P.J., Jakobsson, A. Cardiovascular disease prediction using support vector machines.

  29. Austin, P. C., Tu, J. V., Ho, J. E., Levy, D., & Lee, D. S. (2013). Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology, 66(4), 398–407. doi:10.1016/j.jclinepi.2012.11.008.

    Article  PubMed  Google Scholar 

  30. Denny, J. C., Ritchie, M. D., Crawford, D. C., Schildcrout, J. S., Ramirez, A. H., Pulley, J. M., Basford, M. A., Masys, D. R., Haines, J. L., & Roden, D. M. (2010). Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation, 122(20), 2016–2021. doi:10.1161/circulationaha.110.948828.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Karnik, S., Tan, S. L., Berg, B., Glurich, I., Zhang, J., Vidaillet, H. J., Page, C. D., & Chowdhary, R. (2012). Predicting atrial fibrillation and flutter using electronic health records. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference, 2012, 5562–5565. doi:10.1109/embc.2012.6347254.

    Google Scholar 

  32. Turner, S. D., Berg, R. L., Linneman, J. G., Peissig, P. L., Crawford, D. C., Denny, J. C., Roden, D. M., McCarty, C. A., Ritchie, M. D., & Wilke, R. A. (2011). Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS ONE, 6(5), e19586. doi:10.1371/journal.pone.0019586.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Peissig, P., Linneman, J. (2012). High-density lipoproteins (HDL). http://phekb.org/phenotype/high-density-lipoproteins-hdl. Accessed 30 June 2014

  34. Newton, K. M., Peissig, P. L., Kho, A. N., Bielinski, S. J., Berg, R. L., Choudhary, V., Basford, M., Chute, C. G., Kullo, I. J., Li, R., Pacheco, J. A., Rasmussen, L. V., Spangler, L., & Denny, J. C. (2013). Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. Journal of the American Medical Informatics Association: JAMIA, 20(e1), e147–154. doi:10.1136/amiajnl-2012-000896.

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Conflict of Interest

No competing interests exist.

Human Subjects/Informed Consent Statement

No human studies were carried out by the author for this article

Animal Studies

No animal studies were carried out by the author for this article.

Sources of Funding

The support for this work was provided by NHGRI grant U01HG006388 and NCATS grant 8UL1TR000150-05.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Luke V. Rasmussen.

Additional information

Editor-in-Chief Jennifer L. Hall oversaw the review of this article

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rasmussen, L.V. The Electronic Health Record for Translational Research. J. of Cardiovasc. Trans. Res. 7, 607–614 (2014). https://doi.org/10.1007/s12265-014-9579-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12265-014-9579-z

Keywords

Navigation