Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mining electronic health records: towards better research applications and clinical care

Nature Reviews Genetics volume 13, pages 395–405 (2012)Cite this article

Subjects

Key Points

  • Electronic health record (EHR) systems are increasingly being implemented all over the world, but represent a vast, underused data resource for biomedical research.

  • Structured EHR data, such as encoded diagnosis and medication information, are the easiest data sources to process, but advances in text-mining methods has made it possible to also use the narrative parts of patient records.

  • Statistical studies of the distribution and co-occurrence of clinical features in large collections of patient records enables identification of correlations between, for example, diseases (comorbidities) or between medications and adverse drug reactions.

  • Knowledge-discovery and machine-learning methods can be used both for discovering novel patterns in patient data and for classification and predictive purposes, such as outcome or risk assessment. This has the potential to extend current EHR decision support systems, which integrate available patient data with clinical guidelines to provide assistance to the physician at the point of care.

  • Research platforms built on EHR data, alone or coupled to genotype data, provide an inexpensive and timely way to sample relevant case and control cohorts based on relevant clinical features. As EHR and DNA databases become increasingly interlinked, genotype–phenotype association studies may be designed and conducted by re-using existing data.

  • The growing political focus on the adoption of EHR systems must be accompanied by funding and strategic research into data standards, interoperability and security. Legal matters such as data ownership, privacy and consent need to be addressed to find the right balance between public demands for autonomy and privacy, and manageable procedures for researchers to access data.

  • Fulfilling the full potential of electronic health data for scientific discovery and improved public health will require collaboration across stakeholders and research groups.

Abstract

Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype–phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Electronic health record content.
Figure 2: Four ways to analyse EHR data.

References

  1. Stewart, W. F., Shah, N. R., Selna, M. J., Paulus, R. A. & Walker, J. M. Bridging the inferential gap: the electronic health record and clinical evidence. Health Aff. 26, w181–w191 (2007).

    Article  Google Scholar 

  2. Hillestad, R. et al. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health Aff. 24, 1103–1117 (2005).

    Article  Google Scholar 

  3. Prokosch, H.-U. & Ganslandt, T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf. Med. 1, 38–44 (2009).

    Google Scholar 

  4. Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nature Rev. Genet. 12, 417–428 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. Kush, R. D., Helton, E., Rockhold, F. W. & Hardison, C. D. Electronic health records, medical research, and the Tower of Babel. N. Eng. J. Med. 358, 1738–1740 (2008).

    Article  CAS  Google Scholar 

  6. Taylor, P. When consent gets in the way. Nature 456, 32–33 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Himmelstein, D. U., Wright, A. & Woolhandler, S. Hospital computing and the costs and quality of care: a national study. Am. J. Med. 123, 40–46 (2010).

    Article  PubMed  Google Scholar 

  8. Buntin, M. B., Burke, M. F., Hoaglin, M. C. & Blumenthal, D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff. 30, 464–471 (2011).

    Article  Google Scholar 

  9. Sarkar, I. N. Biomedical informatics and translational medicine. J. Transl. Med. 8, 22 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Blumenthal, D. Launching HITECH. N. Eng. J. Med. 362, 382–385 (2010).

    Article  CAS  Google Scholar 

  11. Hunter, J. The Innovative Medicines Initiative: a pre-competitive initiative to enhance the biomedical science base of Europe to expedite the development of new medicines for patients. Drug Discov. Today 13, 371–373 (2008).

    Article  PubMed  Google Scholar 

  12. Coiera, E. Building a National Health IT System from the middle out. J. Am. Med. Inform. Assoc. 16, 271–273 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Morrison, Z., Robertson, A., Cresswell, K., Crowe, S. & Sheikh, A. Understanding contrasting approaches to nationwide implementations of electronic health record systems: England, the USA and Australia. J. Healthc. Engin. 2, 25–41 (2010).

    Article  CAS  Google Scholar 

  14. Jha, A. K., DesRoches, C. M., Kralovec, P. D. & Joshi, M. S. A progress report on electronic health records in US hospitals. Health Aff. 29, 1951–1957 (2010).

    Article  Google Scholar 

  15. Serdén, L., Lindqvist, R. & Rosén, M. Have DRG-based prospective payment systems influenced the number of secondary diagnoses in health care administrative data? Health Policy 65, 101–107 (2003).

    Article  PubMed  Google Scholar 

  16. Thygesen, L. C., Daasnes, C., Thaulow, I. & Bronnum-Hansen, H. Introduction to Danish (nationwide) registers on health and social issues: structure, access, legislation, and archiving. Scand. J. Public Health 39, 12–16 (2011). An overview of Danish health and socio-economic registries and research possibilities as an example of extensive population-wide registration.

    Article  PubMed  Google Scholar 

  17. Frank, L. When an entire country is a cohort. Science 287, 2398–2399 (2000).

    Article  CAS  PubMed  Google Scholar 

  18. Øyen, N. et al. Recurrence of congenital heart defects in families. Circulation 120, 295–301 (2009).

    Article  PubMed  Google Scholar 

  19. Masutani, Y., MacMahon, H. & Doi, K. Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans. Med. Imaging. 21, 1517–1523 (2002).

    Article  PubMed  Google Scholar 

  20. Hoffman, M. The genome-enabled electronic medical record. J. Biomed. Inform. 40, 44–46 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. Sax, U. & Schmidt, S. Integration of genomic data in Electronic Health Records — opportunities and dilemmas. Methods Inform. Med. 44, 546–550 (2005).

    Article  CAS  Google Scholar 

  22. Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C. & Hurdle, J. F. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb. Med. Inform. 2008, 128–144 (2008). An introduction to NLP and information extraction in the challenging clinical context, which also reviews the relevant research in the field.

    Article  Google Scholar 

  23. Rosenbloom, S. T. et al. Data from clinical notes: a perspective on the tension between structure and flexible documentation. J. Am. Med. Inform. Assoc. 8, 181–186 (2011). A summary of the conflicting views on structured and narrative health data in the context of how to produce valuable and reusable data.

    Article  Google Scholar 

  24. Johnson, S. B. et al. An electronic health record based on structured narrative. J. Am. Med. Inform. Assoc. 15, 54–65 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  25. The International Health Terminology Standards Development Organisation. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). [online]

  26. Bodenreider, O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267–D270 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Savova, G. K. et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17, 507–513 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zeng, Q. T. et al. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med. Inform. Decis. Mak. 6, 30 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Friedman, C., Alderson, P. O., Austin, J. H. M., Cimino, J. J. & Johnson, S. B. A general natural-language text processor for clinical radiology. J. Am. Med. Inform. Assoc. 1, 161–174 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Friedman, C., Shagina, L., Lussier, Y. & Hripcsak, G. Automated encoding of clinical documents based on natural language processing. J. Am. Med. Inform. Assoc. 11, 392–402 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Xu, H. et al. MedEx: a medication information extraction system for clinical narratives. J. Am. Med. Inform. Assoc. 17, 19–24 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ohno-Machado, L. Realizing the full potential of electronic health records: the role of natural language processing. J. Am. Med. Inform. Assoc. 18, 539 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Evans, R. S. et al. A computer-assisted management program for antibiotics and other antiinfective agents. N. Eng. J. Med. 338, 232–238 (1998).

    Article  CAS  Google Scholar 

  34. Demner-Fushman, D., Chapman, W. W. & McDonald, C. J. What can natural language processing do for clinical decision support? J. Biomed. Inform. 42, 760–772 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bellazzi, R. & Zupan, B. Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77, 81–97 (2008). A review of the use of predictive methods in medicine with a special focus on temporal data.

    Article  PubMed  Google Scholar 

  36. Bellazzi, R., Ferrazzi, F. & Sacchi, L. Predictive data mining in clinical medicine: a focus on selected methods and applications. WIREs Data Mining Knowl. Discov. 1, 416–430 (2011).

    Article  Google Scholar 

  37. Lavrac, N. Selected techniques for data mining in medicine. Artif. Intell. Med. 16, 3–23 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. Degroot, V., Beckerman, H., Lankhorst, G. & Bouter, L. How to measure comorbidity. A critical review of available methods. J. Clin. Epidemiol. 56, 221–229 (2003).

    Article  Google Scholar 

  39. Hanauer, D., Rhodes, D. R. & Chinnaiyan, A. M. Exploring clinical associations using “-omics” based enrichment analyses. PLoS ONE 4, e5203 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Roque, F. S. et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput. Biol. 7, e1002141 (2011). Patient stratification and discovery of disease comorbidities and their causes at the molecular level using structured data and text mining on a psychiatric cohort.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Holmes, A. B. et al. Discovering disease associations by integrating electronic clinical data and medical literature. PLoS ONE 6, e21132 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hidalgo, C., Blumm, N., Barabási, A.-L. & Christakis, N. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol. 5, e1000353 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gibbons, R. D. et al. Post-approval drug safety surveillance. Annu. Rev. Public Health 2010, 419–437 (2010).

    Article  Google Scholar 

  44. Lopez-Gonzalez, E., Herdeiro, M. T. & Figueiras, A. Determinants of under-reporting of adverse drug reactions: a systematic review. Drug Saf. 32, 19–31 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Wang, X., Hripcsak, G., Markatou, M. & Friedman, C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J. Am. Med. Inform. Assoc. 16, 328–337 (2009). An example of how text mining of bulk EHR data can be used to uncover statistical correlations between clinical concepts, specifically between medications and ADEs.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Gini, R., Herings, R., Coloma, P. M., Schuemie, M. J. & Trifiro, G. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring : the EU-ADR Project. Pharmacoepidemiol. Drug Saf. 20, 1–11 (2011).

    Article  PubMed  Google Scholar 

  47. Yao, L., Zhang, Y., Li, Y., Sanseau, P. & Agarwal, P. Electronic health records: implications for drug discovery. Drug Discov. Today 16, 594–599 (2011).

    Article  CAS  PubMed  Google Scholar 

  48. Mullins, I. M. et al. Data mining and clinical data repositories: Insights from a 667,000 patient data set. Comput. Biol. Med. 36, 1351–1377 (2006).

    Article  PubMed  Google Scholar 

  49. Wright, A., Chen, E. S. & Maloney, F. L. An automated technique for identifying associations between medications, laboratory results and problems. J. Biomed. Inform. 43, 891–901 (2010).

    Article  PubMed  Google Scholar 

  50. Harpaz, R., Chase, H. S. & Friedman, C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics 11 (Suppl. 9), S7 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Swanson, D. R. Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect. Biol. Med. 30, 7–18 (1986).

    Article  CAS  PubMed  Google Scholar 

  52. Tsuruoka, Y., Miwa, M., Hamamoto, K., Tsujii, J. & Ananiadou, S. Discovering and visualizing indirect associations between biomedical concepts. Bioinformatics 27, 111–119 (2011).

    Article  CAS  Google Scholar 

  53. Oztekin, A., Delen, D. & Kong, Z. J. Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology. Int. J. Med. Inform. 78, e84–e96 (2009).

    Article  PubMed  Google Scholar 

  54. Delen, D., Walker, G. & Kadam, A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34, 113–127 (2005).

    Article  PubMed  Google Scholar 

  55. Kurt, I., Ture, M. & Kurum, A. T. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst. Appl. 34, 366–374 (2008).

    Article  Google Scholar 

  56. Valentino-Devries, J. May the best algorithm win. The Wall Street Journal [online], (2011).

    Google Scholar 

  57. Ohlsson, M., Peterson, C. & Dictor, M. Using hidden Markov models to characterize disease trajectories. Proc. Neural Networks and Expert Systems in Medicine and Healthcare Conference 2001, 324–326 (2001).

    Google Scholar 

  58. Chen, L. L., Blumm, N., Christakis, N. A., Barabási, A.-L. & Deisboeck, T. S. Cancer metastasis networks and the prediction of progression patterns. Br. J. Cancer 101, 749–758 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Fu, T.-C. A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164–181 (2011).

    Article  Google Scholar 

  60. Cao, H., Melton, G. B., Markatou, M. & Hripcsak, G. Use abstracted patient-specific features to assist an information-theoretic measurement to assess similarity between medical cases. J. Biomed. Inform. 41, 882–888 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Melton, G. B. et al. Inter-patient distance metrics using SNOMED CT defining relationships. J. Biomed. Inform. 39, 697–705 (2006).

    Article  PubMed  Google Scholar 

  62. Murphy, S. et al. Instrumenting the health care enterprise for discovery research in the genomic era. Genome Res. 19, 1675–1681 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Murphy, S. N. et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J. Am. Med. Inform. Assoc. 17, 124–130 (2010). A thorough description of the architecture and capabilities of the i2b2 research platform for biomedical research based on EHR data.

    Article  PubMed  PubMed Central  Google Scholar 

  64. McCarty, C. A. et al. The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4, 13 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kho, A. N. et al. Electronic medical records for genetic research: results of the eMERGE consortium. Science Transl. Med. 3, 79re1 (2011).

    Article  Google Scholar 

  66. Schildcrout, J. S. et al. An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records. J. Biomed. Inform. 43, 914–923 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kurreeman, F. et al. Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. Am. J. Hum. Genet. 88, 57–69 (2011). The i2b2 platform put to use for case–control generation and study design based on EHR and DNA data in a rheumatoid arthritis project.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kullo, I. J. et al. Complement receptor 1 gene variants are associated with erythrocyte sedimentation rate. Am. J. Hum. Genet. 89, 131–138 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Kullo, I. J., Ding, K., Jouni, H., Smith, C. Y. & Chute, C. G. A genome-wide association study of red blood cell traits using the electronic medical record. PLoS ONE 5, 9 (2010).

    Article  CAS  Google Scholar 

  70. Denny, J. C. et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Am. J. Hum. Genet. 89, 529–542 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Ritchie, M. D. et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am. J. Hum. Genet. 86, 560–572 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Perlis, R. H. et al. Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol. Med. 42, 41–50 (2012).

    Article  CAS  PubMed  Google Scholar 

  73. Kho, A. N. et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J. Am. Med. Inform. Assoc. 19, 212–218 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Himes, B. E., Dai, Y., Kohane, I. S., Weiss, S. T. & Ramoni, M. F. Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records. J. Am. Med. Inform. Assoc. 16, 371–379 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Roden, D. M. et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin. Pharmacol. Ther. 84, 362–369 (2008). A description of the technical, scientific and legal aspects of the development of an EHR–DNA linked research database with an opt-out consent model.

    Article  CAS  PubMed  Google Scholar 

  76. Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26, 1205–1210 (2010). A demonstration of how EHR data linked with DNA data can be used in a reversal of the normal GWAS approach to search for disease phenotypes associated with SNPs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wilke, R. et al. The emerging role of electronic medical records in pharmacogenomics. Clin. Pharmacol. Ther. 89, 379–386 (2011).

    Article  CAS  PubMed  Google Scholar 

  78. Al Mallah, A., Guelpa, P., Marsh, S. & van Rooij, T. Integrating genomic-based clinical decision support into electronic health records. Personalized Med. 7, 163–170 (2010).

    Article  Google Scholar 

  79. McCarty, C. A. & Wilke, R. A. Biobanking and pharmacogenomics. Pharmacogenomics 11, 637–641 (2010).

    Article  CAS  PubMed  Google Scholar 

  80. Schwarz, U. I. et al. Genetic determinants of response to warfarin during initial anticoagulation. N. Eng. J. Med. 358, 999–1008 (2008).

    Article  CAS  Google Scholar 

  81. Onitilo, A. et al. Estrogen receptor genotype is associated with risk of venous thromboembolism during tamoxifen therapy. Breast Cancer Res. Treat. 115, 643–650 (2009).

    Article  CAS  PubMed  Google Scholar 

  82. Lage, K. et al. Dissecting spatio-temporal protein networks driving human heart development and related disorders. Mol. Syst. Biol. 6, 1–9 (2010).

    Article  Google Scholar 

  83. Greenblum, S., Turnbaugh, P. J. & Borenstein, E. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc. Natl Acad. Sci. USA. 109, 594–599 (2012).

    Article  CAS  PubMed  Google Scholar 

  84. Rzhetsky, A., Wajngurt, D., Park, N. & Zheng, T. Probing genetic overlap among complex human phenotypes. Proc. Natl Acad. Sci. USA. 104, 11694–11699 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Goh, K.-I. et al. The human disease network. Proc. Natl Acad. Sci. USA. 104, 8685–8690 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Park, J., Lee, D.-S., Christakis, N. A. & Barabási, A.-L. The impact of cellular networks on disease comorbidity. Mol. Syst. Biol. 5, 262 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Ashley, E. A. et al. Clinical assessment incorporating a personal genome. Lancet 375, 1525–1535 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Hood, L., Heath, J. R., Phelps, M. E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science 306, 640–643 (2004).

    Article  CAS  PubMed  Google Scholar 

  89. Galas, D. J. & Hood, L. Systems biology and emerging technologies will catalyze the transition from reactive medicine to predictive, personalized, preventive and participatory (P4) medicine. Interdisciplinary Bio Central 1, 6 (2009).

    Article  Google Scholar 

  90. Hall, M. A. Property, privacy, and the pursuit of interconnected electronic medical records. Iowa Law Review 2010, 631–663 (2010).

    Google Scholar 

  91. Noble, S. et al. Feasibility and cost of obtaining informed consent for essential review of medical records in large-scale health services research. J. Health Serv. Res. Policy 14, 77–81 (2009).

    Article  PubMed  Google Scholar 

  92. Kho, M. E., Duffett, M., Willison, D. J., Cook, D. J. & Brouwers, M. C. Written informed consent and selection bias in observational studies using medical records: systematic review. BMJ 338, 1–8 (2009).

    Article  Google Scholar 

  93. Hoffman, S. Balancing privacy, autonomy, and scientific needs in electronic health records research. Case Research Paper Series in Legal Studies [online], (2011). An extensive summary of legal and ethical issues encountered in health research and their potential consequences for conducting scientific research.

    Google Scholar 

  94. Meystre, S. M., Friedlin, F. J., South, B. R., Shen, S. & Samore, M. H. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Med. Res. Methodol. 10, 1–16 (2010).

    Article  Google Scholar 

  95. Benitez, K. & Malin, B. Evaluating re-identification risks with respect to the HIPAA privacy rule. J. Am. Med. Inform. Assoc. 17, 169–177 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Heeney, C., Hawkins, N., de Vries, J., Boddington, P. & Kaye, J. Assessing the privacy risks of data sharing in genomics. Public Health Genomics 14, 17–25 (2011).

    Article  CAS  PubMed  Google Scholar 

  97. Homer, N. et al. Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet. 4, e1000167 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Malin, B. & Sweeney, L. Re-identification of DNA through an automated linkage process. Proc. AMIA Symp. 2001, 423–427 (2001).

    Google Scholar 

  99. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Rothstein, M. A. Is deidentification sufficient to protect health privacy in research? Am. J. Bioeth. 10, 3–11 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Begoyan, A. An overview of interoperability standards for electronic health records. In Integrated Design and Process Technology (IDPT-2007) (2007).

    Google Scholar 

  102. Goossen, W., Goossen-Baremans, A. & van der Zel, M. Detailed clinical models: a review. Healthc. Inform. Res. 16, 201–214 (2010). An introduction to modelling and representation of clinical concepts and meaning, which is important for data interoperability.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Knaup, P., Bott, O., Kohl, C., Lovis, C. & Garde, S. Electronic patient records: moving from islands and bridges towards electronic health records for continuity of care. Yearb. Med. Inform. 2007, 34–46 (2007).

    Article  Google Scholar 

  104. Garde, S., Knaup, P., Hovenga, E. & Heard, S. Towards semantic interoperability for electronic health records. Methods Inf. Med. 46, 332–343 (2007).

    Article  PubMed  Google Scholar 

  105. Wicks, P., Vaughan, T. E., Massagli, M. P. & Heywood, J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nature Biotech. 29, 411–414 (2011).

    Article  CAS  Google Scholar 

  106. Aronson, R. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc. AMIA Symp. 2001, 17–21 (2001).

    Google Scholar 

  107. Uzuner, O., Goldstein, I., Luo, Y. & Kohane, I. Identifying patient smoking status from medical discharge records. J. Am. Med. Inform. Assoc. 15, 14–24 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Uzuner, O. Recognizing obesity and comorbidities in sparse data. J. Am. Med. Inform. Assoc. 16, 561–570 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Uzuner, O., Solti, I. & Cadag, E. Extracting medication information from clinical text. J. Am. Med. Inform. Assoc. 17, 514–518 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Uzuner, O., South, B. R., Shen, S. & Duvall, S. L. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inform. Assoc. 18, 552–557 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  111. Fung, K. W., McDonald, C. & Bray, B. E. RxTerms - a drug interface terminology derived from RxNorm. Proc. AMIA Symp. 2008, 227–231 (2008).

    PubMed Central  Google Scholar 

  112. Steindel, S. J. International classification of diseases, 10th edition, clinical modification and procedure coding system: descriptive overview of the next generation HIPAA code sets. J. Am. Med. Inform. Assoc. 17, 274–282 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank U. Buddrus for kindly providing unpublished data on the adoption of EHR systems in Europe. Any errors in communicating these insights are the sole responsibility of the authors. The authors were supported in part by the Villum Kann Rasmussen Foundation, the Novo Nordisk Foundation and the Danish Research Council for Strategic Research.

Author information

Authors and Affiliations

  1. NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Peter B. Jensen, Lars J. Jensen & Søren Brunak

  2. Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark

    Søren Brunak

Authors
  1. Peter B. Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lars J. Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Søren Brunak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Søren Brunak.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Glossary

Clinical decision support

(CDS). Software systems providing support for decision making to physicians through the application of health knowledge and logical rules to patient data.

Biobanks

Central repositories of biological material that are mainly used for research. They facilitate the re-use of collected samples in different research projects.

Electronic health records

(EHRs). In this review we do not distinguish between EHRs, Electronic Patient Records (EPRs) and Electronic Medical Records (EMRs).

HITECH act

Part of the American Recovery and Reinvestment act from 2009. The Health Information Technology for Economic and Clinical Health (HITECH) act allocates funding and attention to HIT infrastructure and electronic health record adoption and research in the United States.

ICD

The International Classification of Diseases (ICD) published by the World Health Organization. It has been translated into numerous languages.

Medicare

A US government health insurance programme primarily covering people aged 65 yearsand older.

Charlson index

A measure of the accumulated disease burden for a patient. It is calculated as a weighted sum of 22 selected medical conditions that are assigned scores depending on the severity of the condition.

Pharmacovigilance

Monitoring of adverse drug events during clinical trials and after marketing in order to prevent harm to patients. It is typically based on statistical pattern-finding in databases of reported adverse events.

Adverse drug event

(ADE). Used in pharmacology to describe any unexpected or harmful event associated with a given medication.

Feature vector

The representation of objects (patients) as vectors in the space of all relevant features. Each dimension of the vector specifies the association of a patient with a certain feature.

Clustering

A common task in statistical data exploration using measures of similarity between data points, network topology or other methods to group data points with similar characteristics together in clusters.

Semantic similarity

A measure of the similarity of two concepts in terms of their meaning or semantic content. Often quantified using topological measures of distance in an ontology of concepts, such as WordNet or Systematized Nomenclature of Medicine — Clinical Terms (SNOMED CT).

Electronic Medical Records and Genomics Network

(eMERGE Network). An institutional network that is exploring the potential of electronic health record data in genetic and medical research. Participating institutions are: GroupHealth, Geisinger, Marshfield Clinic, Mayo Clinic, Mount Sinai School of Medicine, Northwestern University and Vanderbilt University.

Pharmacogenomics

The study of how genetic variants influence the effects of drugs on, for example, drug metabolism, efficacy and toxicity, with the goal of improving and personalizing drug therapy.

Million Veteran Program

A research project initiated by the Veterans Affairs Office of Research and Development that is aimed at establishing a database with DNA and health record data from one million people. Participation is opt-in.

Kaiser RPGEH

The Kaiser Permanente Research Project on Genes, Environment and Health (Kaiser RPGEH). This project aims to establish a research database with genetic data, environment data and health record data from 500,000 people. Participation is opt-in.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jensen, P., Jensen, L. & Brunak, S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13, 395–405 (2012). https://doi.org/10.1038/nrg3208

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3208

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing