Abstract
Attention on “big data” spans nursing and the health sciences, and extends as well to engineering/computer sciences through to the liberal arts in professional literature. A current Google search (3 Nov 2016) of “big data” yields 288 million entries. A focused search of “big data and nursing” yields more than 3.9 million entries. Thus we ask, “Why big data? Why nursing?” The focus of this chapter is to provide an overview of why big data has emerged now and to make the case for how big data has the capacity to change health, healthcare systems, and nursing. This chapter lays a foundation for the chapters and case studies to follow that explore what data, knowledge, and transformation processes are needed to put information and knowledge into the hands of nursing wherever nurses are working. In this chapter we examine the big data sources within and beyond nursing and healthcare that can be collected and analyzed to improve nursing and patient, family and community health. This chapter entices the reader to examine “Why big data now?” and “Why big data in the future?” This chapter is meant to stir curiosity for “Why should I be knowledgeable?” Whether the reader’s role is in clinical practice, education, research, industry, or policy, the applied uses of big data analytics are empowering change at an exponential speed across all domains. Big data has the capacity to illuminate nursing’s discovery of new knowledge and best practices that are safe, effective and lead to improved outcomes including well-being of providers; it also can expand nursing’s vision and future possibilities through increasing awareness of what nursing doesn’t know. The importance of nursing’s lens on the new discoveries obtained through big data and data science is critical to the transformation of health and healthcare systems. This transformation completes the challenge of placing the person at the center of all care initiatives and actions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brennan PF, Bakken S. Nursing needs big data and big data needs nursing. J Nurs Scholarsh. 2015;47(5):477–84. doi:10.1111/jnu.12159.
Brust A. Cloud data warehouse race heats up. ZDNet. 2015, Jun 26. http://www.zdnet.com/article/cloud-data-warehouse-race-heats-up/. Accessed 10 Nov 2016.
Chatterjee AB. Intrinsic limitations of the human mind. Int J Basic Appl Sci. 2012;1(4):578–83. doi:10.14419/ijbas.v1i4.418.
Chi C, Wang J, Clancy T, Robinson J, Tonellato P, Adam T. Big data cohort extraction to facilitate machine learning to improve statin treatment. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Delaney C, Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017;39(1).
Docherty S, Vorderstrasse A, Brandon D, Johnson C. Visualization of multidimensional data in nursing science. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Feng J. 5 best practices for Tableau and Hadoop. http://www.tableau.com/learn/whitepapers/5-best-practices-tableau-hadoop. Accessed 10 Nov 2016.
Feng J. Databricks application spotlight: Tableau software. 2014. https://databricks.com/blog/2014/10/15/application-spotlight-tableau-software.html. Accessed 10 Nov 2016.
Henschen D. Cloudera brings role-based security to Hadoop. Information Week. 2013, Jul 24. http://www.informationweek.com/big-data/software-platforms/cloudera-brings-role-based-security-to-hadoop/d/d-id/1110903. Accessed 10 Nov 2016.
Hertzberg V, Mac V, Elon L, Mutic A, Peterman K, Mutic N, Tovar-Aguilar JA, Economos J, Flocks J, McCauley L. Novel analytic methods needed for real-time continuous core body temperature data. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Heudecker N, Feinberg D, Adrian M, Palanca T, Greenwald R. Gartner magic quadrant for operational database management systems. 2016. https://info.microsoft.com/CO-SQL-CNTNT-FY16-09Sep-14-MQOperational-Register.html. Accessed 10 Nov 2016.
Hey T, Tansley S, Toll K, editors. The fourth paradigm: data-intensive scientific discovery. Seattle (WA): Microsoft Corporation; 2009.
Kaelber D, Foster W, Gilder J, Love T, Jain A. Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data. J Am Med Inform Assoc. 2012;19(6):965–72. doi:10.1136/amiajnl-2011-000782.
Kim H, Jang I, Quach J, Richardson A, Kim J, Choi J. Explorative analyses of nursing research data. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Kurzweil K. The singularity is near: when humans transcend biology. Westminster, London: Penguin Books; 2006.
Matney S, Settergren T, Carrington J, Richesson R, Sheide A, Westra B. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017
Mayer-Schönberger V, Cukier K. Big data: a revolution that will change how we live, work and think. London: John Murray; 2013.
Miller W, Groves D, Knopf A, Otte J, Silverman R. Word adjacency graph modeling: separating signal from noise in big data. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Monsen K, Peterson J, Mathiason M, Kim E, Votava B, Pieczkiewicz D. Discovering public health nurse-specific family home visiting intervention patterns using visualization technique. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
National Institutes of Health/National Institute of Nursing Research (NIH/NINR). https://www.ninr.nih.gov/researchandfunding/datascience. Accessed 10 Nov 2016.
NIH. https://datascience.nih.gov/; https://datascience.nih.gov/bd2k. Accessed 10 Nov 2016.
NINR. NINR strategic plan: advancing science, improving lives. https://nihrecord.nih.gov/newsletters/2016/07_15_2016/story7.htm. Accessed 10 Nov 2016.
Phillips L, Deroche C, Rantz M, Alexander G, Skubic M, Despins L, Casanova-Abbott C, Harris B, Galambos C, Koopman R. Using embedded sensors in independent living to predict gait changes and falls. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Stelland C. Top 8 big data trends for 2016. 2015. http://www.tableau.com/asset/top-8-trends-big-data-2016?utm_campaign=Prospecting-BGDATA-ALL-ALL&utm_medium=Paid+Search&utm_source=Google+Search&utm_language=EN&utm_country=USCA&kw=%2Btop%20%2B8%20%2Bbig%20%2Bdata%20%2Btrends&adgroup=CTX-Big+Data-Big+Data+All-B&adused=106945857375&matchtype=b&placement=&kcid=512788ef-9c83-4023-9baf-541715034e38&gclid=CJGpiaj31dACFUYbaQodYYgHqA. Accessed 10 Nov 2016.
Topaz M, Radhakrishnan K, Blackley S, Lei V, Lai K, Zhou L. Studying associations between heart failure self-management and rehospitalizations using natural language processing. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Topol E. The patient will see you now. New York: Basic Books; 2015.
Vasant D. Data science & prediction. Commun ACM. 2013;56(12):64–73.
Wallace M. All the things: data visualization in a world of connected devices. 2015. http://www.tableau.com/about/blog/2015/1/all-things-data-visualization-world-connected-devices-36393. Accessed 10 Nov 2016.
Westra, BL, Sylvia M, Weinfurter EF, Pruinelli L, Park JI, Dodd D, Keenan GM, Senk P, Richesson RL, Baukner V, Cruz C, Gao G, Whittenburg L, Delaney CW (2017). Big Data Science: A Literature Review of Nursing Research Exemplars. Nursing Outlook. 2016 Dec 8. poi: S0029-6554(16)30396-7. doi: 10.1016/j.outlook.2016.11.021. [Epub ahead of print]
Wheatley M. AtScale’s Hadoop maturity survey highlights big data’s relentless growth. 2015. http://siliconangle.com/blog/2015/09/17/atscales-hadoop-maturity-survey-highlights-big-datas-relentless-growth/. Accessed 10 Nov 2016.
Wilkie D, Khokhar A, Lodhi M, Yao Y, Ansari R, Keenan G. Framework for mining and analysis of standardized nursing care plan data. In Delaney C and Westra B. Big data: data science in nursing. West J Nurs Res (Special Issue). 2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Delaney, C.W., Simpson, R.L. (2017). Why Big Data?: Why Nursing?. In: Delaney, C., Weaver, C., Warren, J., Clancy, T., Simpson, R. (eds) Big Data-Enabled Nursing. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-53300-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-53300-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53299-8
Online ISBN: 978-3-319-53300-1
eBook Packages: MedicineMedicine (R0)