International Journal of Knowledge Management and Practices

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Big Data Analytics in Healthcare:Challenges and Possibilities


Affiliations
1 KIIT School of Management, Bhubaneswar, Odisha, India
2 KIIT School of Management, KIIT University, Bhubaneswar, Odisha, India
     

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With the improvement in technology, the data acquisition capability, its storage and analysis have gone up manifold. However, the costs related to such activities have gone down significantly. This leads to the abundance of data everywhere. Notwithstanding the data omni-presence, users process data selectively, take intuitive decisions, work under both data-overload and data-poverty.

Arguably, the healthcare sector has enormous challenges in achieving universal basic health services, providing safe, effective, affordable and timely intervention for patients. The sector involves communication between various stakeholders such as government, healthcare institutions, doctors, patients, and insurance companies. This byzantine, recursive, and helical process generates enormous data.

In healthcare practice one of the most important issues is that the big data creation is not purposive; availability of data may not be for the purpose for its use. Secondly, if less data is equally sufficient to make a good quality decision, then big data may bring in confusion. Thirdly, at a conceptual level, statistics relies on effective sampling methods to generalize and predict about population. Big data analytics also uses above principles, indicating that the availability of data alone is not enough. Another daunting challenge is data ownership; data gatherers’ claim of exclusivity is unethical which raises questions about privacy, user rights, and public ownership etc. From the business model perspective, the data creation becomes free and thus marginal propensity to consume increases exponentially.

This paper takes a contrarian approach and presents the challenges and critics towards the implementation of big data processes in healthcare.


Keywords

Big Data, Healthcare, Analytics, Patient-Centric, Governance, Signal to Noise.
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  • Bailar, J. C., & Hoaglin, D. C. (2012). Medical uses of statistics. Hoboken, NJ: John Wiley & Sons.

  • Belle, A., Thiagarajan, R., Soroushmehr, S. M., Navidi, F., Beard, D. A., & Najarian, K. (2015). Big data analytics in healthcare. BioMed Research International, 1-16. doi:10.1155/2015/370194

  • Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. BMJ Quality & Safety, 12(6), 458-464.

  • Big Data in Healthcare Made Simple. (2018, October 17). Retrieved December 10, 2018 from https://www.healthcatalyst.com/big-data-in-healthcare-made-simple

  • Bryant, S. L. (2004). The information needs and information seeking behaviour of family doctors. Health Information & Libraries Journal, 21(2), 84-93. doi:10.1111/j.1471-1842.2004.00490.x

  • Coiera, E. (2006). Communication systems in healthcare. Clinical Biochemist Reviews, 27(2), 89.

  • Dinov, I. D. (2016). Methodological challenges and analytic opportunities for modelling and interpreting Big Healthcare Data. Giga Science, 5(1), 12. doi:10.1186/s13742-016-0117-6

  • Faltin, F., Kenett, R. S., & Ruggeri, F. (Eds.). (2012). Statistical methods in healthcare. West Sussex, UK: John Wiley & Sons.

  • Hellesø, R., Lorensen, M., & Sorensen, L. (2004). Challenging the information gap - The patients transfer from hospital to home health care. International Journal of Medical Informatics, 73(7-8), 569-580. doi:10.1016/j.ijmedinf.2004.04.009

  • Hill, K. (2012). How Target figured out a teen girl was pregnant before her father did. Forbes, Inc. How big data servers are different from regular dedicated hosting servers. (2018, October 3). Retrieved December 10, 2018 from http://bigdata-madesimple.com/how-big-data-servers-are-different-from-regular-dedicated-hostingservers/

  • Komorowski, M. (2014, March 9). Retrieved December 2, 2018 from http://www.mkomo.com/cost-per-gigabyte-update

  • Korsch, B. M., & Negrete, V. F. (1972). Doctor-patient communication. Scientific American, 227(2), 66-74. doi:10.1038/scientificamerican0872-66

  • Kripalani, S., LeFevre, F., Phillips, C. O., Williams, M. V., Basaviah, P., & Baker, D. W. (2007). Deficits in communication and information transfer between hospital-based and primary care physicians. JAMA, 297(8), 831-841. doi:10.1001/jama.297.8.831

  • Lluch, M. (2011). Healthcare professionals’ organisational barriers to health information technologies - A literature review. International Journal of Medical Informatics, 80(12), 849-862. doi:10.1016/j.ijmedinf.2011.09.005

  • Machine Learning Healthcare Applications - 2018 and Beyond. (2018, September 16). Retrieved December 10, 2018 from https://www.techemergence.com/machine-learning-healthcare-applications/

  • Mearian, L. (2014, March 14). Data storage - Then and now. Retrieved December 2, 2018 from https://www.computerworld.com/article/2473980/data-storage-solutions/data-storage-solutions-143723-storage-now-and-then.html

  • Middleton, B. (2005). Achieving U.S. health information technology adoption: The need for a third hand. Health Affairs, 24(5), 1269-1272. doi:10.1377/hlthaff.24.5.1269

  • Middleton, P., Kjeldsen, P., & Tully, J. (2013). Forecast: The internet of things. Worldwide. Gartner.

  • Miller, E. A., & West, D. M. (2009). Where’s the revolution? Digital technology and health care in the internet age. Journal of Health Politics, Policy and Law, 34(2), 261-284. doi:10.1215/03616878-2008-046

  • Pett, M. A. (2016). Nonparametric statistics for health care research: Statistics for small samples and unusual distributions. Thousand Oaks, CA: Sage.

  • Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2006). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage.

  • Ponemon.org. (2018). Retrieved August 6, 2018 from https://www.ponemon.org/local/upload/file/Sixth%20Annual%20Patient%20Privacy%20%26%20Data%20Security%20Report%20FINAL%206.pdf

  • Reiser, S. J., & Reiser, S. J. (1981). Medicine and the reign of technology. Cambridge: Cambridge University Press.

  • Rovner, D. R. (1991). Medical informatics, computer applications in health care. Edited by Edward H. Shortliffe, Leslie E. Perreault, Gio Wiederhold, and Lawrence M. Fagan, Reading, MA: Addison-Wesley, 1990. Medical Decision Making, 11(2), 148-148. doi:10.1177/0272989x9101100215

  • Saha, S., Beach, M. C., & Cooper, L. A. (2008). Patient centeredness, cultural competence and healthcare quality. Journal of the National Medical Association, 100(11), 1275-1285. doi:10.1016/s0027-9684(15)31505-4

  • Sheingold, B. H., & Hahn, J. A. (2014). The history of healthcare quality: The first 100 years 1860-1960. International Journal of Africa Nursing Sciences, 1, 18-22. doi:10.1016/j.ijans.2014.05.002

  • Stewart, M. A. (1995). Effective physician-patient communication and health outcomes: A review. CMAJ: Canadian Medical Association Journal, 152(9), 1423.

  • Stewart, M. A., Brown, J. B., Boon, H., Galajda, J., Meredith, L., & Sangster, M. (1999). Evidence on patient-doctor communication. Cancer, 25(1999), 30.

  • Szlezák, N., Evers, M., Wang, J., & Pérez, L. (2014). The role of big data and advanced analytics in drug discovery, development, and commercialization. Clinical Pharmacology & Therapeutics, 95(5), 492-495. doi:10.1038/clpt.2014.29

  • Topol, E. J., & Hill, D. (2012). The creative destruction of medicine: How the digital revolution will create better health care (p. 2). New York, NY: Basic Books. Unstructured Data In Healthcare. (n.d.). Retrieved December 5, 2018 from https://artificial-intelligence.healthcaretechoutlook.com/cxoinsights/unstructured-data-in-healthcare-nid-506.html

  • Waitzkin, H. (1984). Doctor-Patient Communication. JAMA, 252(17), 2441-2446. doi:10.1001/jama.1984.03350170043017

  • Ward, J. S., & Barker, A. (2013). Undefined by data: A survey of big data definitions. Retrieved December 20, 2018 from https://arxiv.org/pdf/1309.5821.pdf

  • Wears, R. L., & Berg, M. (2005). Computer technology and clinical work. JAMA, 293(10), 1261-1263. doi:10.1001/jama.293.10.1261

  • Westbrook, J. I., Braithwaite, J., Iedema, R., & Coiera, E. W. (2004, September). Evaluating the impact of information communication technologies on complex organizational systems: A multi-disciplinary, multi-method framework. In Medinfo (1323-1327).

  • Where Healthcare’s Big Data Actually Comes From. (2018, January 11). Retrieved December 20, 2018 from https://www.techemergence.com/where-healthcaresbig-data-actually-comes-from/

  • Widup, S., Spitler, M., Hylender, D., & Bassett, G. (2018). 2018 Verizon Data Breach Investigations Report.

  • Wilson, S., Ruscoe, W., Chapman, M., & Miller, R. (2001). General practitioner-hospital communications: A review of discharge summaries. Journal of Quality in Clinical Practice, 21(4), 104-108. doi:10.1046/j.1440-1762.2001.00430.x


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  • Big Data Analytics in Healthcare:Challenges and Possibilities

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Authors

Manoj Kumar Jena
KIIT School of Management, Bhubaneswar, Odisha, India
Brajaballav Kar
KIIT School of Management, KIIT University, Bhubaneswar, Odisha, India

Abstract


With the improvement in technology, the data acquisition capability, its storage and analysis have gone up manifold. However, the costs related to such activities have gone down significantly. This leads to the abundance of data everywhere. Notwithstanding the data omni-presence, users process data selectively, take intuitive decisions, work under both data-overload and data-poverty.

Arguably, the healthcare sector has enormous challenges in achieving universal basic health services, providing safe, effective, affordable and timely intervention for patients. The sector involves communication between various stakeholders such as government, healthcare institutions, doctors, patients, and insurance companies. This byzantine, recursive, and helical process generates enormous data.

In healthcare practice one of the most important issues is that the big data creation is not purposive; availability of data may not be for the purpose for its use. Secondly, if less data is equally sufficient to make a good quality decision, then big data may bring in confusion. Thirdly, at a conceptual level, statistics relies on effective sampling methods to generalize and predict about population. Big data analytics also uses above principles, indicating that the availability of data alone is not enough. Another daunting challenge is data ownership; data gatherers’ claim of exclusivity is unethical which raises questions about privacy, user rights, and public ownership etc. From the business model perspective, the data creation becomes free and thus marginal propensity to consume increases exponentially.

This paper takes a contrarian approach and presents the challenges and critics towards the implementation of big data processes in healthcare.


Keywords


Big Data, Healthcare, Analytics, Patient-Centric, Governance, Signal to Noise.

References