Integration of Genomics Data and Electronic Health Records Toward Personalized Medicine: A Targeted Review

Ali Najafi, Neda Emami, Taha Samad-Soltani
656

Views


Abstract

Introduction: Integration of rapidly expanding high-throughput omics technologies and electronic health record (EHR) has created an unprecedented advantage in terms of acquiring routine healthcare data to accelerate genetic discovery. In this regard, EHR can also provide several important advantages to omics research if the integration challenges are well handled. The main purpose of the present study was to review available and published knowledge in the related literature and then to classify and discuss stakeholders’ requirements in this domain.

Material and Methods: At first, a broad electronic search of all available literature in English was conducted on the topic through a search in the databases of Medline, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), Scopus, and Cochrane. Then, stakeholders’ requirements were tabulated, and finally, a word cloud was generated and analyzed to achieve functional and non-functional cases.

Results: A total of 81 articles were included in the given analysis. Integration requirements also consisted of nine functional cases including a uniform approach to the interpretation of genetic tests, standardized terminologies and ontologies, structured data entry as much as possible, an integrated online patient portal, multiple data source handling, machine-readable storing and reporting, research-oriented requirements, pharmacogenomics decision support capabilities, and phenotyping algorithms and knowledge base. Besides, there were three non-functional cases comprised of interoperability of multiple systems, ethical, legal, security factor, and big data computations.

Conclusion: The main challenges in this way could also have semantic and technical themes. Therefore, system developers could guarantee the success of systems by overcoming the given challenges.



Keywords

Genetics; Personalized; Precision; Electronic Record; Pharmacogenomics

References

Ginsburg, GS, Willard HF. Genomic and personalized medicine: foundations and applications. Transl Res. 2009; 154(6): 277-87. PMID: 19931193 DOI: 10.1016/j.trsl.2009.09.005

Jain KK. Non-genomic aspects of personalized Medicine. In: Jain KK. Textbook of personalized medicine. Springer; 2021.

Downing GJ. Key aspects of health system change on the path to personalized medicine. Transl Res. 2009; 154(6): 272-6. PMID: 19931192 DOI: 10.1016/j.trsl.2009.09.003

Wei W-Q, Denny JC. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome Med. 2015; 7(1): 41. PMID: 25937834 DOI: 10.1186/s13073-015-0166-y

Joyner MJ, Paneth N. Seven questions for personalized medicine. JAMA. 2015; 314(10): 999-1000. PMID: 26098474 DOI: 10.1001/jama.2015.7725

Bourgey M, Dali R, Eveleigh R, Chen KC, Letourneau L, Fillon J, et al. GenPipes: An open-source framework for distributed and scalable genomic analyses. GigaScience. 2019; 8(6): giz037. PMID: 31185495 DOI: 10.1093/gigascience/giz037

Monda KL, Chen GK, Taylor KC, Palmer C, Edwards TL, Lange LA, et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet. 2013; 45(6): 690-6. PMID: 23583978 DOI: 10.1038/ng.2608

Howey R, Shin S-Y, Relton C, Smith GD, Cordell HJ. Bayesian network analysis complements Mendelian randomization approaches for exploratory analysis of causal relationships in complex data. PLoS Genet. 2020; 16(3): e1008198. PMID: 32119656 DOI: 10.1371/journal.pgen.1008198

Postmus I, Trompet S, Deshmukh HA, Barnes MR, Li X, Warren HR, et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nat Commun. 2014; 5: 5068. PMID: 25350695 DOI: 10.1038/ncomms6068

Henderson GE, Cadigan RJ, Edwards TP, Conlon I, Nelson AG, Evans JP, et al. Characterizing biobank organizations in the U.S.: Results from a national survey. Genome Med. 2013; 5(1): 3. PMID: 23351549 DOI: 10.1186/gm407

Delaney JT, Ramirez AH, Bowton E, Pulley JM, Basford MA, Schildcrout JS, et al. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin Pharmacol Ther. 2012; 91(2): 257-63. PMID: 22190063 DOI: 10.1038/clpt.2011.221

Monnin P, Legrand J, Husson G, Ringot P, Tchechmedjiev A, Jonquet C, et al. PGxO and PGxLOD: A reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison. BMC Bioinformatics. 2019; 20(Suppl 4): 139. PMID: 30999867 DOI: 10.1186/s12859-019-2693-9

Salem J-E, Shoemaker MB, Bastarache L, Shaffer CM, Glazer AM, Kroncke B, et al. Association of thyroid function genetic predictors with atrial fibrillation: A phenome-wide association study and inverse-variance weighted average meta-analysis. JAMA Cardiol. 2019; 4(2): 136-43. PMID: 30673079 DOI: 10.1001/jamacardio.2018.4615

Mueller A. Wordcloud for python documentation [Internet]. 2018 [cited: 15 Mar 2021]. Available from: http://amueller.github.io/word_cloud/index.html

Arlow J, Neustadt I. UML 2 and the unified process: Practical object-oriented analysis and design. Pearson Education; 2005.

Shoenbill K, Fost N, Tachinardi U, Mendonca EA. Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations. J Am Med Inform Assoc. 2014; 21(1): 171-80. PMID: 23771953 DOI: 10.1136/amiajnl-2013-001694

Moore B, Rynearson S, Cunningham F, Ritchie G, Eilbeck K. Using GVF for clinical annotation of personal genomes. AIMM; 2012.

Sitapati A, Kim H, Berkovich B, Marmor R, Singh S, El-Kareh R, et al. Integrated precision medicine: The role of electronic health records in delivering personalized treatment. Wiley Interdiscip Rev Syst Biol Med. 2017; 9(3): 1378. PMID: 28207198 DOI: 10.1002/wsbm.1378

Ullman-Cullere MH, Mathew JP. Emerging landscape of genomics in the electronic health record for personalized medicine. Hum Mutat. 2011; 32(5): 512-6. PMID: 21309042 DOI: 10.1002/humu.21456

Marsolo K, Spooner SA. Clinical genomics in the world of the electronic health record. Genet Med. 2013; 15(10): 786-91. PMID: 23846403 DOI: 10.1038/gim.2013.88

Deckard J, McDonald CJ, Vreeman DJ. Supporting interoperability of genetic data with LOINC. J Am Med Inform Assoc. 2015; 22(3): 621-7. PMID: 25656513 DOI: 10.1093/jamia/ocu012

Hazin R, Brothers KB, Malin BA, Koenig BA, Sanderson SC, Rothstein MA, et al. Ethical, legal, and social implications of incorporating genomic information into electronic health records. Genet Med. 2013; 15(10): 810-6. PMID: 24030434 DOI: 10.1038/gim.2013.117

Kullo IJ, Jarvik GP, Manolio TA, Williams MS, Roden DM. Leveraging the electronic health record to implement genomic medicine. Genet Med. 2013; 15(4): 270-1. PMID: 23018749 DOI: 10.1038/gim.2012.131

Crump JK, Fiol GD, Williams MS, Freimuth RR. Prototype of a standards-based EHR and genetic test reporting tool coupled with HL7-compliant infobuttons. AMIA Jt Summits Transl Sci Proc. 2018; 2017: 330-9. PMID: 29888091

Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet. 2011; 12(6): 417-28. PMID: 21587298 DOI: 10.1038/nrg2999

Scheuermann RH, Milgrom H. Personalized care, comparative effectiveness research and the electronic health record. Curr Opin Allergy Clin Immunol. 2010; 10(3): 168-70. PMID: 20431366 DOI: 10.1097/ACI.0b013e328338c232

Shabo Shvo A. Meaningful use of pharmacogenomics in health records: semantics should be made explicit. Pharmacogenomics. 2010; 11(1): 81-7. PMID: 20017674 DOI: 10.2217/pgs.09.161

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(10): 772-8. PMID: 24071798 DOI: 10.1038/gim.2013.131

Barros IM, Alcântara TS, Mesquita AR, Santos ACO, Paixão FP, Lyra JrDP. The use of pictograms in the health care: A literature review. Res Social Adm Pharm. 2014; 10(5): 704-19. PMID: 24332470 DOI: 10.1016/j.sapharm.2013.11.002

Peterson JF, Bowton E, Field JR, Beller M, Mitchell J, Schildcrout J, et al. Electronic health record design and implementation for pharmacogenomics: A local perspective. Genet Med. 2013; 15(10): 833-41. PMID: 24009000 DOI: 10.1038/gim.2013.109

Sheldon J, Ou W. The real informatics challenges of personalized medicine: Not just about the number of central processing units. Per Med. 2013; 10(7): 639-45. PMID: 29768753 DOI: 10.2217/pme.13.16

Sethi P, Theodos K. Translational bioinformatics and healthcare informatics: computational and ethical challenges. Perspect Health Inf Manag. 2009; 6(Fall): 1h. PMID: 20169020 PMCID: PMC2804463

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(6): 1173-8. PMID: 26198304 DOI: 10.1093/jamia/ocv045

Kim E, Rubinstein SM, Nead KT, Wojcieszynski AP, Gabriel PE, Warner JL. The evolving use of electronic health records (EHR) for research. Semin Radiat Oncol. 2019; 29(4): 354-61. PMID: 31472738 DOI: 10.1016/j.semradonc.2019.05.010

Safarova MS, Kullo IJ. Using the electronic health record for genomics research. Curr Opin Lipidol. 2020; 31(2): 85-93. PMID: 32073412 DOI: 10.1097/MOL.0000000000000662

Wells BJ, Chagin KM, Nowacki AS, Kattan MW. Strategies for handling missing data in electronic health record derived data. EGEMS (Wash DC). 2013; 1(3): 1035. PMID: 25848578 DOI: 10.13063/2327-9214.1035

Marwala T. Computational intelligence for missing data imputation, estimation and management: Knowledge optimization techniques. Information Science Reference; 2009.

Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, et al. Big data from electronic health records for early and late translational cardiovascular research: Challenges and potential. Eur Heart J. 2018; 39(16): 1481-95. PMID: 29370377 DOI: 10.1093/eurheartj/ehx487

Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, et al. The electronic medical records and genomics (eMERGE) network: Past, present, and future. Genet Med. 2013; 15(10): 761-71. PMID: 23743551 DOI: 10.1038/gim.2013.72

Denny JC. Surveying recent themes in translational bioinformatics: Big data in EHRs, omics for drugs, and personal genomics. Yearb Med Inform. 2014; 9(1): 199-205. PMID: 25123743 DOI: 10.15265/IY-2014-0015

Levy KD, Decker BS, Carpenter JS, Flockhart DA, Dexter PR, Desta Z, et al. Prerequisites to implementing a pharmacogenomics program in a large health-care system. Clin Pharmacol Ther. 2014; 96(3): 307-9. PMID: 24807457 DOI: 10.1038/clpt.2014.101

Roden DM, Denny JC. Integrating electronic health record genotype and phenotype datasets to transform patient care. Clin Pharmacol Ther. 2016; 99(3): 298-305. PMID: 26667791 DOI: 10.1002/cpt.321

Koumaditis K, Themistocleous M, Rupino Da Cunha P. SOA implementation critical success factors in healthcare. Journal of Enterprise Information Management. 2013; 26(4): 343-62.

Downing GJ, Boyle SN, Brinner KM, Osheroff JA. Information management to enable personalized medicine: Stakeholder roles in building clinical decision support. BMC Med Inform Decis Mak. 2009; 9(1): 44. PMID: 19814826 DOI: 10.1186/1472-6947-9-44

Kuehn BM. Pilot programs seek to integrate genomic data into practice. JAMA. 2017; 318(5): 410-2. PMID: 28700793 DOI: 10.1001/jama.2017.7181

Bender D, Sartipi K. HL7 FHIR: An agile and restful approach to healthcare information exchange. International Symposium on Computer-Based Medical Systems. IEEE; 2013.

Janjua NK, Hussain M, Afzal M, Ahmad HF. Digital health care ecosystem: SOA compliant HL7 based health care information interchange. International Conference on Digital Ecosystems and Technologies. IEEE; 2009.

Alonso SG, de la Torre Díez I, Rodrigues JJPC, Hamrioui S, López-Coronado M. A systematic review of techniques and sources of big data in the healthcare sector. J Med Syst. 2017; 41(11): 183. PMID: 29032458 DOI: 10.1007/s10916-017-0832-2

Wiewiórka MS, Messina A, Pacholewska A, Maffioletti S, Gawrysiak P, Okoniewski MJ. SparkSeq: Fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision. Bioinformatics. 2014; 30(18): 2652-3. PMID: 24845651 DOI: 10.1093/bioinformatics/btu343

Chung W-C, Chen C-C, Ho J-M, Lin C-Y, Hsu W-L, Wang Y-C, et al. CloudDOE: A user-friendly tool for deploying Hadoop clouds and analyzing high-throughput sequencing data with MapReduce. PLoS One. 2014; 9(6): e98146. PMID: 24897343 DOI: 10.1371/journal.pone.0098146

Reynolds SM, Miller M, Lee P, Leinonen K, Paquette SM, Rodebaugh Z, et al. The ISB cancer genomics cloud: A flexible cloud-based platform for cancer genomics research. Cancer Res. 2017; 77(21): e7-10. PMID: 29092928 DOI: 10.1158/0008-5472.CAN-17-0617

Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: Data quality issues and informatics opportunities. Summit Transl Bioinform. 2010; 2010: 1-5. PMID: 21347133 PMCID: PMC3041534

Lan K, Wang D-T, Fong S, Liu L-S, Wong KKL, Dey N. A survey of data mining and deep learning in bioinformatics. J Med Syst. 2018; 42(8): 139. PMID: 29956014 DOI: 10.1007/s10916-018-1003-9

Angulo C, Crespo P, Maldonado JA, Moner D, Pérez D, Abad I, et al. Non-invasive lightweight integration engine for building EHR from autonomous distributed systems. Int J Med Inform. 2007; 76 (Suppl 3): S417-24. PMID: 17600763 DOI: 10.1016/j.ijmedinf.2007.05.002




DOI: https://doi.org/10.30699/fhi.v10i1.299

Refbacks

  • There are currently no refbacks.