Abstract
Today, with the technology-driven developments, healthcare systems and services are being radically transformed to become more effective and efficient. Omics technologies along with mobile sensors and monitoring systems are emerging disruptive technologies, which will provide us the opportunities of a paradigm shifting in medical theory, research and practice. Traditional methods are beginning to convert to a new personalized, predictive, preventive and participatory paradigm based on big data approaches. We anticipate that; next-generation health information systems will be constructed based on tracking all aspects of health status on 24/7, and returning evidence based recommendations to empower individuals. As an example of future personal health record (PHR) concept, GO-WELL is based on clinical envirogenomic knowledge base (CENG-KB) to engage patients for predictive care. In this chapter, we present the design principles of this system, after describing several concepts, including personalized medicine, omics revolution, incorporation of genomic data into medical decision processes, and the utilization of enviro-behavioural parameters for disease risk assessment.
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Ahuja, J.K., Moshfegh, A.J., Holden, J.M., et al.: USDA food and nutrient databases provide the infrastructure for food and nutrition research, policy, and practice. J. Nutr. 143(2), 241S–249S (2013)
Alspach, J.G.: The importance of family health history: your patients’ and your own. Crit. Care Nurse 31(1), 10–15 (2011)
Aronson, S.J., Clark, E.H., Varugheese, M., et al.: Communicating new knowledge on previously reported genetic variants. Genet. Med. (2012). doi:10.1038/gim.2012.19
Aronson, S.J., Clark, E.H., Babb, L.J., et al.: GeneInsight Suite: a platform to support laboratory and provider use of DNA-based genetic testing. Hum. Mutat. 32, 532–536 (2011)
Balshaw, D.M., Kwok, R.K.: Innovative methods for improving measures of the personal environment. Am. J. Prev. Med. 42, 558–559 (2012)
Barnes, M.R.: Genetic variation analysis for biomedical researchers: a primer. In: Barnes, M.R., Breen, G. (eds.) Genetic Variation: Methods and Protocols. Humana Press (2010) (Methods in Molecular Biology)
Berg, J.S., Khoury, M.J., Evans, J.P.: Deploying whole genome sequencing in clinical practice and public health: Meeting the challenge one bin at a time. Genet. Med. 13, 499–504 (2011)
Bloss, C.S., Schork, N.J., Topol, E.J.: Effect of direct-to-consumer genome-wide profiling to assess disease risk. N. Engl. J. Med. 364, 34–524 (2011)
Boyd, L.K., Mao, X., Lu, Y.L.: The complexity of prostate cancer: genomic alterations and heterogeneity. Nat. Rev. Urol. 11, 64–652 (2012)
Van Camp, C.M., Hayes, L.B.: Assessing and increasing physical activity. J. Appl. Behav. Anal. 45, 871–875 (2012)
Dogac, A., Yuksel, M., Avcı, A., et al.: Electronic health record interoperability as realized in the Turkish health information system. Methods Inf. Med. 50, 9–140 (2011)
Downing, G.J.: Key aspects of health system change on the path to personalized medicine. Transl. Res. 154, 272–276 (2009)
Drmanac, R.: The ultimate genetic test. Science 336, 1110–1112 (2012)
Dziuda, D.M.: Data Mining for Genomics and Proteomics, Analysis of Gene and Protein Expression Data. Wiley, New York (2010)
Feero, W.G., Bigley, M.B., Brinner, K.M.: New standards and enhanced utility for family health history information in the electronic health record: an update from the American health information community’s family health history multi-stakeholder workgroup. J. Am. Med. Inform. Assoc. 15, 723–728 (2008)
Feero, W.G.: Genomics, health care, and society. N. Engl. J. Med. 365, 1033–1041 (2011)
Feldman, B., Martin, E.M., Skotnes, T.: Big Data in Healthcare, Hype and Hope, Dr. Bonnie 360\(^{\circ }\) (2012)
Ferlay, J., Shin, H.R., Bray, F., et al.: GLOBOCAN 2008 v1.2, Cancer incidence, mortality and prevalence worldwide. In: IARC CancerBase No. 10. (2008). http://globocan.iarc.fr/factsheet.asp. Accessed October 2013
Fox, S., Duggan, M.: Mobile health 2012, pew internet & American life project (2012). http://www.pewinternet.org/Reports/2012/Mobile-Health.aspx. Accessed Mar 2013
Garets, D., Davis, M.: Electronic medical records vs. electronic health records: yes, there is a difference. HIMSS Analytics (2006). http://www.himssanalytics.org/docs/WP_EMR_EHR.pdf. Accessed Jun 2013
Ginsburg, G.S., Willard, H.F.: Genomic and personalized medicine: foundations and applications. Transl. Res. 154, 87–277 (2009)
Glaser, J., Henley, D.E., Downing, G., et al.: Advancing personalized health care through health information technology: an update from the American Health Information Community’s Personalized Health Care Workgroup. J. Am. Med. Inform. Assoc. 15, 6–391 (2008)
Green, R.C., Rehm, H.L., Kohane, I.S.: Clinical genome sequencing. In: Ginsburg, G.S., Willard, H.F. (eds.) Genomic and Personalized Medicine, 2nd edn. Academic Press, New York (2013)
Gubb, E., Matthiesen, R.: Introduction to Omics. In: Matthiesen, R. (ed.) Bioinformatics Methods in Clinical Research, Methods in Molecular Biology, Humana Press, Totowa (2010)
Guttmacher, A.E., Collins, F.S., Carmona, R.H.: The family history-more important than ever. N. Engl. J. Med. 351, 2333–2336 (2004)
Hamilton, C.M., Strader, L.C., Pratt, J.G., et al.: The PhenX Toolkit: get the most from your measures. Am. J. Epidemiol. 174, 253–260 (2011)
Haskell, W.L., Troiano, R.P., Hammond, J.A., et al.: Physical activity and physical fitness: standardizing assessment with the PhenX Toolkit. Am. J. Prev. Med. 42, 92–486 (2012)
Hayrinen, K., Sarantoa, K., Nykanen, P.: Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int. J. Med. Inform. 77, 291–304 (2008)
Helgason, A., Stefánsson, K.: The past, present, and future of direct-to-consumer genetic tests. Dialogues Clin. Neurosci. 12, 61–68 (2010)
Hoffman, M., Arnoldi, C., Chuang, I.: The clinical bioinformatics ontology: a curated semantic network utilizing RefSeq information. Pac. Symp. Biocomput. 10, 139–150 (2005)
Hoffman, M.A.: The genome-enabled electronic medical record. J. Biomed. Inform. 40, 44–46 (2007)
Hoffman, M.A., Williams, M.S.: Electronic medical records and personalized medicine. Hum. Genet. 130, 33–39 (2011)
IOM (Institute of Medicine).: Evidence-Based Medicine and the Changing Nature of Healthcare: 2007 IOM Annual Meeting Summary. The National Academies Press, Washington (2008)
IOM (Institute of Medicine).: Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. The National Academies Press, Washington (2009)
Kahn, S.D.: On the future of genomic data. Science 331, 728–729 (2011)
Kose, I., Akpınar, N., Gürel, M., et al.: Turkey’s national health information system (NHIS). In: Proceedings of the eChallenges Conference, Stockholm, s.n., pp. 170–177 (2008)
Lioy, P.J., Rappaport, S.M.: Exposure science and the exposome: an opportunity for coherence in the environmental health sciences. Environ. Health Perspect. 119, A466–467 (2011)
Marian, A.J.: Medical DNA sequencing. Curr. Opin. Cardiol. 26, 175–180 (2011)
Masys, D.R., Jarvik, G.P., Abernethy, N.F., et al.: Technical desiderata for the integration of genomic data into electronic health records. J. Biomed. Inform. 45, 419–422 (2012)
Merchant, A.T., Dehghan, M.: Food composition database development for between country comparisons. Nutr. J. 5, 2 (2006)
Miksad, R.A., Bubley, G., Church, P., et al.: Prostate cancer in a transgender woman 41 years after initiation of feminization. JAMA 296, 2316–2317 (2006)
Miliard, M.: IBM helps Coriell Institute keep cool. Healthcare IT News (2011). Available at http://www.healthcareitnews.com/news/ibm-helps-coriell-institute-keep-cool. Accessed Oct 2013
National Cancer Institute.: Risk Factors for Prostate Cancer Development (2013). Available at http://www.cancer.gov/cancertopics/pdq/prevention/prostate/healthprofessional/page3. Accessed Oct 2013
Ng, S.W., Popkin, B.M.: Monitoring foods and nutrients sold and consumed in the United States: dynamics and challenges. J. Acad. Nutr. Diet. 112, 41–45 (2012)
O’Driscoll, A., Daugelaite, J., Sleator, R.D.: ‘Big data’, Hadoop and cloud computing in genomics. J. Biomed. Inform. 46, 81–774 (2013)
Paddock, C.: Self-tracking rools help you stay healthy. Medical News Today (2013). http://www.medicalnewstoday.com/articles/254902.php. Accessed Jun 2013
Pan, H., Tryka, K.A., Vreeman, D.J., et al.: Using PhenX measures to identify opportunities for cross-study analysis. Hum. Mutat. 33, 849–857 (2012)
Pennington, J.A., Stumbo, P.J., Murphy, S.P., et al.: Food composition data: the foundation of dietetic practice and research. J. Am. Diet. Assoc. 107, 2105–2113 (2007)
Poo, D.C., Cai, S., Mah, J.T.: UASIS: universal automatic SNP identification system. BMC Genomics 12(Suppl 3), S9 (2011)
Post, R.C., Herrup, M., Chang, S., et al.: Getting plates in shape using SuperTracker. J. Acad. Nutr. Diet. 112, 354–358 (2012)
Rappaport, S.M., Smith, M.T.: Epidemiology, environment and disease risks. Science 330, 460–461 (2010)
Riegelman, R.K.: Public health 101: healthy people-healthy populations Jones & Bartlett Learning (2010).
Rutishauser, I.H.: Dietary intake measurements. Public Health Nutr. 8(7A), 1100–11007 (2005)
Sartor, A.O.: Risk factors for prostate cancer, In: UpToDate. Vogelzang, N., Lee, R., Richie, J.P.: (ed.) (2013). http://www.uptodate.com/contents/risk-factors-for-prostate-cancer. Accessed: March 2013
Sax, U., Schmidt, S.: Integration of genomic data in electronic health records, opportunities and dilemmas. Methods Inf. Med. 44, 546–550 (2005)
Scheuner, M.T., de Vries, H., Kim, B., et al.: Are electronic health records ready for genomic medicine? Genet. Med. 11, 510–517 (2009)
Schneider, M.V., Orchard, S.: Omics technologies, data and bioinformatics principles. In: Mayer, B. (ed.) Bioinformatics for Omics Data. HumanaPress (Methods and Protocols) (2011)
Schultz, T.: Turning healthcare challenges into big data opportunities: a use-case review across the pharmaceutical development lifecycle. Bull. Assoc. Inf. Sci. Technol. 39, 34–40 (2013)
Starren, J., Williams, M.S., Bottinger, E.P.: Crossing the omic chasm: a time for omic ancillary systems. JAMA 309, 1237–1238 (2013)
Stover, P.J., Harlan, W.R., Hammond, J.A., et al.: PhenX: a toolkit for interdisciplinary genetics research. Curr. Opin. Lipidol. 21, 136–140 (2010)
Stumbo, P.J., Weiss, R., Newman, J.W., et al.: Web-enabled and improved software tools and data are needed to measure nutrient intakes and physical activity for personalized health research. J. Nutr. 140, 2104–2115 (2010)
Swan, M.: Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator. Netw. 1, 217–253 (2012)
Thomas, P.E., Klinger, R., Furlong, L.I., et al.: Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers. BMC Bioinf. 12(Suppl 4), S4 (2011)
Van Tongeren, M., Cherrie, J.W.: An integrated approach to the exposome. Environ. Health Perspect. 120, A103–104 (2012)
Tran, B.Q., Gonzales, P.: Standards and guidelines for personal health records in the United States: finding consensus in a rapidly evolving and divided environment. J. Health Med. Inf. (2012). doi:10.4172/2157-7420
Transforming Health Care through Big Data, Institute for Health Technology Transformation (IHT2), New York, 2013. https://iht2bigdata2013.questionpro.com. Accessed Oct 2013
Ullman-Cullere, M.H., Mathew, J.P.: Emerging landscape of genomics in the electronic health record for personalized medicine. Hum. Mutat. 32, 512–516 (2011)
Verdonk, P., Klinge, I.: Mainstreaming sex and gender analysis in public health genomics. Gend. Med. 9, 402–410 (2012)
Weitzel, J.N., Blazer, K.R., MacDonald, D.J., et al.: Genetics, genomics, and cancer risk assessment, state of the art and future directions in the era of personalized medicine. CA Cancer J. Clin. 61, 327–359 (2011)
Wild, C.P.: The exposome: from concept to utility. Int. J. Epidemiol. 41, 24–32 (2012)
Winter, A., Reinhold, H., Ammenwerth, E.: Health Information Systems, Architectures and Strategies, 2nd edn. Springer, London (2011)
Wright, C., Burton, H., Hall, A., et al.: The implications of whole genome sequencing for health in the UK. PHG Foundation (2011)
Yao, L., Zhang, Y., Li, Y., et al.: Electronic health records, implications for drug discovery. Drug Discov. Today 16, 13–14 (2011)
Yücebaş, C., Aydın Son, Y.: A prostate cancer model build by a novel SVM-ID3 hybrid feature selection method using both genotyping and phenotype data from dbGaP, PLOS ONE, (2013). doi:10.1371/journal.pone.0091404
Zheng, S.L., Sun, J., Wiklund, F., et al.: Cumulative association of five genetic variants with prostate cancer. N. Engl. J. Med. 358, 910–919 (2008)
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Beyan, T., Aydın Son, Y. (2014). Emerging Technologies in Health Information Systems: Genomics Driven Wellness Tracking and Management System (GO-WELL). In: Bessis, N., Dobre, C. (eds) Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-319-05029-4_13
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DOI: https://doi.org/10.1007/978-3-319-05029-4_13
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