Skip to main content
SpringerLink
Log in

The Genomic Medical Record and Omic Ancillary Systems

  • Chapter
  • First Online:
Personalized and Precision Medicine Informatics

Part of the book series: Health Informatics ((HI))

Abstract

“Omic” data become increasingly clinically relevant for a variety of PPM goals such as personalized diagnosis, assessment of disease risk, or targeting appropriate medications and doses. EHRs today are designed to manage conventional clinical data and not omic data (i.e., they are not omic-enabled). The present chapter presents informatics challenges and solutions for omic data storage, analysis and use in clinical PPM workflows. Omic ancillary systems (OASs), in particular may serve these purposes. An OAS allows storage for large files containing results, as well as specialized tools and processing pipelines to aid in their interpretation. The chapter discusses Data and Knowledge Management, Display of Information, Implementation, Standards, as well as Ethical, and Health Economics challenges and considerations.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Concert Genetics. The current landscape of genetic testing: market growth, reimbursement trends, challenges and opportunities [Internet]. http://www.concertgenetics.com/wp-content/uploads/2018/04/12_ConcertGenetics_CurrentLandscapeOfGeneticTesting2018.pdf.

  2. Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42:D980–5.

    Article  CAS  Google Scholar 

  3. Yang S, Lincoln SE, Kobayashi Y, Nykamp K, Nussbaum RL, Topper S. Sources of discordance among germ-line variant classifications in ClinVar. Genet Med. 2017;19:1118–26.

    Article  Google Scholar 

  4. Shirts BH, Salama JS, Aronson SJ, Chung WK, Gray SW, Hindorff LA, et al. CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record. J Am Med Inf Assoc. 2015;22:1231–42.

    Google Scholar 

  5. Whelton PK, Carey RM, Aronow WS, Casey DE Jr, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American College of Cardiology/American Heart Association Task Force. Hypertension. 2018;71:1269–324.

    Article  CAS  Google Scholar 

  6. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013;15:565–74. http://www.nature.com/articles/gim201373.

    Article  CAS  Google Scholar 

  7. Kalia SS, Adelman K, Bale SJ, Chung WK, Eng C, Evans JP, et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19:249–55. http://www.ncbi.nlm.nih.gov/pubmed/27854360.

    Article  Google Scholar 

  8. Starren J, Williams MS, Bottinger EP. Crossing the omic chasm: a time for omic ancillary systems. JAMA. 2013;309:1237–8.

    Article  CAS  Google Scholar 

  9. Masys DR, Jarvik GP, Abernethy NF, Anderson NR, Papanicolaou GJ, Paltoo DN, et al. Technical desiderata for the integration of genomic data into electronic health records. J Biomed Inform. 2012;45:419–22. http://www.ncbi.nlm.nih.gov/pubmed/22223081.

    Article  Google Scholar 

  10. Hoffman MA. The genome-enabled electronic medical record. J Biomed Inf. 2007;40:44–6.

    Article  CAS  Google Scholar 

  11. Warner JL, Jain SK, Levy MA. Integrating cancer genomic data into electronic health records. Genome Med. 2016;8:113.

    Article  Google Scholar 

  12. Jacob HJ, Abrams K, Bick DP, Brodie K, Dimmock DP, Farrell M, et al. Genomics in clinical practice: lessons from the front lines. Sci Transl Med. 2013;5:194cm5.

    Article  Google Scholar 

  13. Korf BR, Berry AB, Limson M, Marian AJ, Murray MF, O’Rourke PP, et al. Framework for development of physician competencies in genomic medicine: report of the Competencies Working Group of the Inter-Society Coordinating Committee for Physician Education in Genomics. Genet Med. 2014;16:804–9.

    Article  Google Scholar 

  14. Kho AN, Rasmussen LV, Connolly JJ, Peissig PL, Starren J, Hakonarson H, et al. Practical challenges in integrating genomic data into the electronic health record. Genet Med. 2013;15:772–8.

    Article  Google Scholar 

  15. Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, et al. A conceptual model for translating omic data into clinical action. J Pathol Inform. 2015;6:46. http://www.ncbi.nlm.nih.gov/pubmed/26430534.

    Article  Google Scholar 

  16. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9. https://www.ncbi.nlm.nih.gov/pubmed/19505943.

    Article  Google Scholar 

  17. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.

    Article  CAS  Google Scholar 

  18. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–11. https://www.ncbi.nlm.nih.gov/pubmed/11125122.

    Article  CAS  Google Scholar 

  19. Caudle KE, Klein TE, Hoffman JM, Muller DJ, Whirl-Carrillo M, Gong L, et al. Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process. Curr Drug Metab. 2014;15:209–17. http://www.ncbi.nlm.nih.gov/pubmed/24479687.

    Article  CAS  Google Scholar 

  20. Cimino JJ. Infobuttons: anticipatory passive decision support. In: AMIA Annual Symposium Proceedings. Washington, DC: American Medical Informatics Association; 2008. p. 1203–4.

    Google Scholar 

  21. Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, et al. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2013;20:489–93. https://www.ncbi.nlm.nih.gov/pubmed/23011124.

    Article  Google Scholar 

  22. Rasmussen-Torvik LJ, Stallings SC, Gordon AS, Almoguera B, Basford MA, Bielinski SJ, et al. Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems. Clin Pharmacol Ther. 2014;96:482–9.

    Article  CAS  Google Scholar 

  23. Dunnenberger HM, Crews KR, Hoffman JM, Caudle KE, Broeckel U, Howard SC, et al. Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers. Annu Rev Pharmacol Toxicol. 2015;55:89–106. http://www.ncbi.nlm.nih.gov/pubmed/25292429.

    Article  CAS  Google Scholar 

  24. Gottesman O, Scott SA, Ellis SB, Overby CL, Ludtke A, Hulot JS, et al. The CLIPMERGE PGx program: clinical implementation of personalized medicine through electronic health records and genomics-pharmacogenomics. Clin Pharmacol Ther. 2013;94:214–7. http://www.ncbi.nlm.nih.gov/pubmed/23588317.

    Article  CAS  Google Scholar 

  25. Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK, et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc. 2014;21:e93–9. http://www.ncbi.nlm.nih.gov/pubmed/23978487.

    Article  Google Scholar 

  26. Heale B, Overby C, Del Fiol G, Rubinstein W, Maglott D, Nelson T, et al. Integrating genomic resources with electronic health records using the HL7 Infobutton standard. Appl Clin Inform. 2016;7:817–31. http://www.ncbi.nlm.nih.gov/pubmed/27579472.

    Article  Google Scholar 

  27. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer-technology—a comparison of 2 theoretical-models. Manag Sci. 1989;35:982–1003.

    Article  Google Scholar 

  28. Goodhue DL, Thompson RL. Task-technology fit and individual-performance. MIS Q. 1995;19:213–36.

    Article  Google Scholar 

  29. Ammenwerth E, Iller C, Mahler C. IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study. BMC Med Inf Decis Mak. 2006;6:–3. http://www.ncbi.nlm.nih.gov/pubmed/16401336.

  30. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13:319–40.

    Article  Google Scholar 

  31. O’Day VL, Bobrow DG, Shirley M. The social-technical design circle. In: Proc 1996 ACM conf comput support coop work. New York: ACM; 1996. p. 160–9.

    Google Scholar 

  32. Baxter G, Sommerville I. Socio-technical systems: from design methods to systems engineering. Interact Comput. 2011;23:4–17. http://iwc.oxfordjournals.org/content/23/1/4.full.pdf.

    Article  Google Scholar 

  33. Koberg D, Bagnall J. The universal traveller: a soft-systems guide to creativity, problem-solving, and the process of reaching goals. Los Altos, CA: Kaufmann; 1976.

    Google Scholar 

  34. Samtools. The variant call format specification [Internet]. 2018. https://samtools.github.io/hts-specs/VCFv4.3.pdf.

  35. Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011;27:718–9.

    Article  Google Scholar 

  36. Health Level Seven International. HL7 version 2.5.1 implementation guide: Lab Results Interface (LRI), release 1, STU release 3—US Realm. 2017.

    Google Scholar 

  37. Health Level Seven International. HL7 version 2 implementation guide: clinical genomics; fully LOINC-qualified genetic variation model, release 2. 2013.

    Google Scholar 

  38. Health Level Seven International. HL7 implementation guide for CDA® release 2: Genetic Testing Report (GTR), DSTU release 1. 2013.

    Google Scholar 

  39. Aronson SJ, Clark EH, Babb LJ, Baxter S, Farwell LM, Funke BH, et al. The GeneInsight Suite: a platform to support laboratory and provider use of DNA-based genetic testing. Hum Mutat. 2011;32:532–6.

    Article  Google Scholar 

  40. Aronson S, Babb L, Ames D, Gibbs RA, Venner E, Connelly JJ, et al. Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example. J Am Med Inform Assoc. 2018;25:1375–81. https://doi.org/10.1093/jamia/ocy051.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Health Level Seven International. Overview—FHIR v3.0.1 [Internet]. 2018. https://www.hl7.org/fhir/overview.html.

  42. Global Alliance for Genomics and Health. Work streams [Internet]. 2018. https://www.ga4gh.org/howwework/workstreams/.

  43. Health Level Seven International. Genomics—FHIR v3.0.1 [Internet]. 2018. https://www.hl7.org/fhir/genomics.html.

  44. Deckard J, McDonald CJ, Vreeman DJ. Supporting interoperability of genetic data with LOINC. J Am Med Inform Assoc. 2015;22:621–7. https://doi.org/10.1093/jamia/ocu012.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Gray KA, Yates B, Seal RL, Wright MW, Bruford EA. Genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015;43:D1079–85.

    Article  CAS  Google Scholar 

  46. Dunnen JT, Dalgleish R, Maglott DR, Hart RK, Greenblatt MS, McGowan-Jordan J, et al. HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat. 2016;37:564–9. https://onlinelibrary.wiley.com/doi/abs/10.1002/humu.22981.

    Article  Google Scholar 

  47. Horaitis O, Cotton RG. The challenge of documenting mutation across the genome: the human genome variation society approach. Hum Mutat. 2004;23:447–52.

    Article  CAS  Google Scholar 

  48. Stevens-Kroef M, Simons A, Rack K, Hastings RJ. Cytogenetic nomenclature and reporting. Methods Mol Biol. 2017;1541:303–9.

    Article  CAS  Google Scholar 

  49. Robarge JD, Li L, Desta Z, Nguyen A, Flockhart DA. The star-allele nomenclature: retooling for translational genomics. Clin Pharmacol Ther. 2007;82:244–8.

    Article  CAS  Google Scholar 

  50. Kalman LV, Agúndez JAG, Appell ML, Black JL, Bell GC, Boukouvala S, et al. Pharmacogenetic allele nomenclature: international workgroup recommendations for test result reporting. Clin Pharmacol Ther. 2016;99:172–85. https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.280.

    Article  CAS  Google Scholar 

  51. Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc. 2016;23:899–908. https://doi.org/10.1093/jamia/ocv189.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Warner JL, Rioth MJ, Mandl KD, Mandel JC, Kreda DA, Kohane IS, et al. SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc. 2016;23:701–10. https://doi.org/10.1093/jamia/ocw015.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Alterovitz G, Warner J, Zhang P, Chen Y, Ullman-Cullere M, Kreda D, et al. SMART on FHIR genomics: facilitating standardized clinico-genomic apps. J Am Med Inform Assoc. 2015;22:1173–8.

    PubMed  Google Scholar 

  54. Office of the National Coordinator for Health Information Technology (ONC). 2015 Edition Health Information Technology (Health IT) certification criteria, 2015 Edition Base Electronic Health Record (EHR) definition, and ONC Health IT certification program modifications [Internet]. p. 62601–759. http://www.federalregister.gov/a/2015-25597/p-307.

  55. Fossey R, Kochan D, Winkler E, Pacyna JE, Olson J, Thibodeau S, et al. Ethical considerations related to return of results from genomic medicine projects: the eMERGE network (phase III) experience. J Pers Med. 2018;8:E2.

    Article  Google Scholar 

  56. Wilhelmsen KC, Schmitt CP, Fecho K. Factors influencing data archival of large-scale genomic data sets. RENCI Tech. Rep. Ser. Chapel Hill, NC: University of North Carolina at Chapel Hill; 2013.

    Google Scholar 

  57. Tsai E, Shakbatyan R, Evans J, Rossetti P, Graham C, Sharma H, et al. Bioinformatics workflow for clinical whole genome sequencing at partners healthcare personalized medicine. J Pers Med. 2016;6:12. http://www.mdpi.com/2075-4426/6/1/12

    Article  Google Scholar 

  58. Dewey FE, Grove ME, Pan C, Goldstein BA, Bernstein JA, Chaib H, et al. Clinical interpretation and implications of whole-genome sequencing. JAMA. 2014;311:1035–45.

    Article  CAS  Google Scholar 

  59. Wetterstrand K. DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP) [Internet]. 2018. https://www.genome.gov/sequencingcostsdata.

Download references

Author information

Authors and Affiliations

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

    Luke V. Rasmussen, Timothy M. Herr, Abdulrahman M. Jahhaf & Justin B. Starren

  2. Division of General Internal Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Casey Overby Taylor

  3. Division of Health Sciences Informatics, Johns Hopkins University, Baltimore, MD, USA

    Casey Overby Taylor

  4. Socio-Technical Innovation, Northwestern University Clinical and Translational Sciences, Northwestern University, Chicago, IL, USA

    Therese A. Nelson

  5. Northwestern University Clinical and Translational Science Institute (NUCATS), Northwestern University, Chicago, IL, USA

    Justin B. Starren

  6. Center for Data Science and Informatics (CDSI), Northwestern University, Chicago, IL, USA

    Justin B. Starren

Authors
  1. Luke V. Rasmussen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothy M. Herr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Casey Overby Taylor
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdulrahman M. Jahhaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Therese A. Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Justin B. Starren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin B. Starren .

Editor information

Editors and Affiliations

  1. Pharmaceutical Care & Health Systems, University of Minnesota Pharmaceutical Care & Health Systems, Minneapolis, MN, USA

    Terrence Adam

  2. Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA

    Constantin Aliferis

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rasmussen, L.V., Herr, T.M., Taylor, C.O., Jahhaf, A.M., Nelson, T.A., Starren, J.B. (2020). The Genomic Medical Record and Omic Ancillary Systems. In: Adam, T., Aliferis, C. (eds) Personalized and Precision Medicine Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-18626-5_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18626-5_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18625-8

  • Online ISBN: 978-3-030-18626-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation