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E-Health

Digital Transformation and its Potential for Healthcare
  • Horst Hahn
  • Andreas Schreiber
Chapter

Summary

While the digital transformation is well underway in numerous areas of society, medicine still faces immense challenges. Nevertheless, the potential resulting from the interaction of modern biotechnology and information technology is huge. Initial signs of the transformation can be seen in numerous places – a transformation that will further be accelerated by the integration of previously separate medical data silos and the focused use of new technologies. In this chapter, we describe the current state of integrated diagnostics and the mechanisms of action behind the emerging field of digital healthcare. One of the areas of focus is the recent revolution caused by artificial intelligence. At the same time, we have seen the emancipation of patients who now have access to an enormous breadth of medical knowledge via social networks, Internet search engines, and healthcare guides and apps. Against this backdrop, we will discuss the change in the doctor-patient relationship as well as the changing roles of doctors and computers, and the resulting business models.

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Sources and literature

  1. [1]
    AHA – American Heart Association (2017) Alexa can tell you the steps for CPR, warning signs of heart attack and stroke. Blog. Zugriff im Juli 2017: http://news.heart.org/alexa-can-tell-you-the-steps-for-cpr-warning-signs-of-heart-attack-and-stroke/
  2. [2]
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. doi:10.1038/ncomms5006
  3. [3]
    ASCO (2017) Clinical Cancer Advances 2017, American Society of Clinical Oncology. Zugriff im Juli 2017: https://www.asco.org/research-progress/reports-studies/clinical-cancer-advances
  4. [4]
    CB Insights (2017) From Virtual Nurses To Drug Discovery: 106 Artificial Intelligence Startups In Healthcare. Zugriff im Juli 2017: https://www.cbinsights.com/blog/artificial-intelligence-startups-healthcare/
  5. [5]
    CMS – Centers for Medicare and Medicaid Services (2017) NHE Fact Sheet. Zugriff im Juli 2017: https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html
  6. [6]
    Cooper DN, Ball EV, Stenson PD et al (2017) HGMD – The Human Gene Mutation Database at the Institute of Medical Genetics in Cardiff. Zugriff im Juli 2017: http://www.hgmd.cf.ac.uk/
  7. [7]
    Destatis – Statistisches Bundesamt (2017) Gesundheitsausgaben der Bundesrepublik Deutschland. Zugriff im Juli 2017: https://www.destatis.de/DE/ZahlenFakten/GesellschaftStaat/Gesundheit/Gesundheitsausgaben/Gesundheitsausgaben.html
  8. [8]
    Dusheck J (2016) Diagnose this – A health-care revolution in the making. Stanford Medicine Journal, Fall 2016. Zugriff im Juli 2017: https://stanmed.stanford.edu/2016fall/the-future-of-health-care-diagnostics.html
  9. [9]
    Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. doi:10.1038/nature21056CrossRefGoogle Scholar
  10. [10]
    Ferrucci D, Levas A, Bagchi S et al (2013) Watson: Beyond Jeopardy! Artificial Intelligence 199:93–105. doi:10.1016/j.artint.2012.06.009CrossRefGoogle Scholar
  11. [11]
    Harz M (2017) Cancer, Computers, and Complexity: Decision Making for the Patient. European Review 25(1):96–106. doi:10.1017/S106279871600048XCrossRefGoogle Scholar
  12. [12]
    Herper M (2017) MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine. Forbes. Zugriff im Juli 2017: https://www.forbes.com/sites/matthewherper/2017/02/19/md-anderson-benches-ibm-watson-in-setback-for-artificial-intelligence-in-medicine
  13. [13]
    Knight W (2017) The Dark Secret at the Heart of AI. MIT Technology Review. Zugriff im Juli 2017: https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
  14. [14]
    Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–6. doi:10.1016/j.ejca.2011.11.036CrossRefGoogle Scholar
  15. [15]
    Mariotto AB, Yabroff KR, Shao Y et al (2011) Projections of the cost of cancer care in the United States: 2010-2020. J Natl Cancer Inst 103(2):117–28. doi:10.1093/jnci/djq495CrossRefGoogle Scholar
  16. [16]
    NIH – National Institutes of Health (2011) Cancer costs projected to reach at least $158 billion in 2020. News Releases. Zugriff im Juli 2017: https://www.nih.gov/news-events/news-releases/cancer-costs-projected-reach-least-158-billion-2020
  17. [17]
    Ryan KJ (2016) Who’s Smartest: Alexa, Siri, and or Google Now? Inc. Zugriff im Juli 2017: https://www.inc.com/kevin-j-ryan/internet-trends-7-most-accurate-word-recognition-platforms.html
  18. [18]
    Sahiner B, Chan HP, Petrick N et al (1996) Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 15(5):598–610. doi:10.1109/42.538937CrossRefGoogle Scholar
  19. [19]
    Schmutzler R, Huster S, Wasem J, Dabrock P (2015) Risikoprädiktion: Vom Umgang mit dem Krankheitsrisiko. Dtsch Arztebl 112(20): A-910–3Google Scholar
  20. [20]
    Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489. doi:10.1038/nature16961CrossRefGoogle Scholar
  21. [21]
    Spear BB, Heath-Chiozzi M, Huff J (2001) Clinical application of pharmacogenetics. Trends Mol Med 7(5):201–4. doi:10.1016/S1471-4914(01)01986-4CrossRefGoogle Scholar
  22. [22]
    Stenson et al. (2017) The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet 136:665-677. doi: 10.1007/s00439-017-1779-6CrossRefGoogle Scholar
  23. [23]
    Tecco H (2017) 2016 Year End Funding Report: A reality check for digital health. Rock Health Funding Database. Zugriff im Juli 2017: https://rockhealth.com/reports/2016-year-end-funding-report-a-reality-check-for-digital-health/
  24. [24]
    The Economist (2017) A digital revolution in healthcare is speeding up. Zugriff im Juli 2017: https://www.economist.com/news/business/21717990-telemedicine-predictive-diagnostics-wearable-sensors-and-host-new-apps-will-transform-how
  25. [25]
    TheStreet (2013) What Information Are We Willing To Share To Improve Healthcare? Intel Healthcare Innovation Barometer. Zugriff im Juli 2017: https://www.thestreet.com/story/12143671/3/what-information-are-we-willing-to-share-to-improve-healthcare-graphic-business-wire.html
  26. [26]
    Topol E (2012) The Creative Destruction of Medicine: How the Digital Revolution will Create Better Health Care. Basic Books, New York. ISBN:978-0465061839Google Scholar
  27. [27]
    Trotter F, Uhlman D (2011) Hacking Healthcare – A Guide to Standards, Workflows, and Meaningful Use. O’Reilly Media, Sebastopol. ISBN:978-1449305024Google Scholar
  28. [28]
    Zhang W, Hasegawa A, Itoh K, Ichioka Y (1991) Image processing of human corneal endothelium based on a learning network. Appl Opt. 30(29):4211–7. doi:10.1364/AO.30.004211CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Horst Hahn
    • 1
  • Andreas Schreiber
    • 1
  1. 1.Fraunhofer Institute for Medical Image Computing MEVISBremenGermany

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