Utilizing Big Data for Health Care Automation: Obligations, Fitness and Challenges

  • Sherin ZafarEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 154)


The impact of big data in healthcare ranges from medical diagnosis to the lifestyle quantification. Ponemon Institute in 2012 declared that around 30% of electronic data comes from the industry of healthcare so the situation is quite alarming for managing this huge amount of big data being generated. As specified that the extraction of knowledge of big data is fast, cheap and quite effective so it can bring a change in patients life by improving health and services. Health care analytics new doors has been opened by big data as it provides answers for “what happened”, “why happened”, “what will happen” and “how to make happen for description, diagnosis and prediction”. This chapter namely “Big data for health care automation, obligation, fitness and challenges” focuses upon the potential knowledge of 4 V’s of big data namely, Volume, Velocity, Variety and Veracity by a radical improvement through productivity bottlenecks being unlocked. This will bring a radical change in the quality and accessibility of health care automation.


Big data Health care automation Diagnosis V’s of big data 


  1. 1.
    Anya, O., Tawfik, H.: Leveraging big data analytics for personalized elderly care. Appl. Comput. Med. Health, 99–124 (2016). Scholar
  2. 2.
    Baker, L.A.: Do our “big data” in genetic analysis need to get bigger? Psychophysiology 51(12), 1321–1322 (2014). Scholar
  3. 3.
    Bakker, L., Aarts, J., Redekop, W.: Is big data in healthcare about big hope or big hype? early health technology assessment of big data analytics in healthcare. Value in Health 19(7), A705 (2016). Scholar
  4. 4.
    Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G.: Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 33(7), 1123–1131 (2014). Scholar
  5. 5.
    Bian, J., Topaloglu, U., Yu, F.: Towards large-scale twitter mining for drug-related adverse events. In: Proceedings of the 2012 International Workshop on Smart Health and Wellbeing—SHB’12 (2012).
  6. 6.
    Blair, L.M.: Publicly available data and pediatric mental health: leveraging big data to answer big questions for children. J. Pediatr. Health Care 30(1), 84–87 (2016). Scholar
  7. 7.
    Brewka, G.: Artificial intelligence—a modern approach by Stuart Russell and Peter Norvig, prentice hall. series in artificial intelligence, Englewood Cliffs, NJ. Knowl. Eng. Rev. 11(01), 78 (1996) Scholar
  8. 8.
    Cercone, F’IEEE, N.: What’s the big deal about big data? Big Data and Inf. Anal. 1(1), 31–79 (2015).
  9. 9.
    Dhar, V.: Big data and predictive analytics in health care. Big Data 2(3), 113–116 (2014). Scholar
  10. 10.
    Gong, A.: Comment on “data science and its relationship to big data and data-driven decision making”. Big Data 1(4), 194–194 (2013). Scholar
  11. 11.
    Marconi, K., Dobra, M., Thompson, C.: The use of big data in healthcare. Big Data Bus. Analytics, 229–248 (2013). Scholar
  12. 12.
    Mahoney, B.: Big earth 2014; big data! In::2012 Conference on Intelligent Data Understanding (2012).
  13. 13.
    Nambiar, R., Bhardwaj, R., Sethi, A., Vargheese, R.: A look at challenges and opportunities of big data analytics in healthcare. In: 2013 IEEE International Conference on Big Data (2013).
  14. 14.
    Nemis-White, J., MacKillop, J., Montague, T.: Canada’s future healthcare: can it be better? will it be better? HealthcarePapers 12(2), 51–59 (2012). Scholar
  15. 15.
    O’Regan, G.: IBM. A brief history of computing, 71–84 (2012). Scholar
  16. 16.
    Raghupathi, W.: Data mining in healthcare. Healthc. Inf. 211–224 (2010). Scholar
  17. 17.
    Ripberger, J.T.: Capturing curiosity: using internet search trends to measure public attentiveness. Policy Stud. J. 39(2), 239–259 (2011). Scholar
  18. 18.
    Ruths, D., Pfeffer, J.: Social media for large studies of behavior. Science 346(6213), 1063–1064 (2014). Scholar
  19. 19.
    Sinnott, R., Duan, H., Sun, Y.: A case study in big data analytics. Big Data, 357–388 (2016). Scholar
  20. 20.
    Schoen, H., Gayo-Avello, D., Takis Metaxas, P., Mustafaraj, E., Strohmaier, M., Gloor, P.: The power of prediction with social media. Internet Res 23(5), 528–543 (2013). Scholar
  21. 21.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty, Fuzziness Knowl-Based Syst. 10(05), 557–570 (2002). Scholar
  22. 22.
    Velthuis, E., Malka, E., Richards, M.: ‘Big data’ in health care. What does it mean and will it make a Difference? Value in Health, 16(7), A479 (2013). Scholar
  23. 23.
    Viju Raghupathi, W.R.: An overview of health analytics. J. Health Med. Inf. 04(03) (2013).
  24. 24.
    Walker, R.: Impact of analytics and big data on corporate culture and recruitment. From Big Data to Big Profits, pp. 184–201 (2015). Scholar
  25. 25.
    White, S.: A review of big data in health care: challenges and opportunities. Open Access Bioinform. 13 (2014).
  26. 26.
    Wilkinson, Z.: Oh, how pinteresting! an introduction to Pinterest. Library Hi Tech News 30(1), 1–4 (2013). Scholar
  27. 27.
    Yasseri, T., Sumi, R., Kertész, J.: Circadian patterns of wikipedia editorial activity: a demographic analysis. PLoS ONE 7(1), e30091 (2012). Scholar
  28. 28.
    White, D.S., Le Cornu, A.: Visitors and residents: a new typology for online engagement. First Monday 16(9) (2011).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of CSE, SESTJamia HamdardNew DelhiIndia

Personalised recommendations