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Big Data: Will It Improve Patient-Centered Care?

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  • Denzil G. Fiebig

    (University of New South Wales)

Abstract

Within a generation, empirical researchers have experienced unprecedented increases in the availability of data. ‘Big data’ has arrived with considerable hype and a sense that these are dramatic shifts in the research environment that have wide-reaching implications across many disciplines. There is no doubt that the analysis of new and varied sources of data currently available to researchers in health have the potential to better measure, monitor and describe health outcomes of patients and to uncover interesting patterns in how patients respond to treatments and interact with the health system. What is less clear is whether answers are readily available to more nuanced and substantive research questions. Here, the data-rich environment needs to be complemented by considerable research effort developing novel research designs and generating new and improved methods of analysis. Importantly, this will require researchers to be able to combine data from multiple sources and to be pro-active in data collection.

Suggested Citation

  • Denzil G. Fiebig, 2017. "Big Data: Will It Improve Patient-Centered Care?," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 10(2), pages 133-139, April.
  • Handle: RePEc:spr:patien:v:10:y:2017:i:2:d:10.1007_s40271-016-0201-0
    DOI: 10.1007/s40271-016-0201-0
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    References listed on IDEAS

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    1. Randall P. Ellis & Denzil G. Fiebig & Meliyanni Johar & Glenn Jones & Elizabeth Savage, 2013. "Explaining Health Care Expenditure Variation: Large‐Sample Evidence Using Linked Survey And Health Administrative Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1093-1110, September.
    2. Denzil G. Fiebig & Stephanie Knox & Rosalie Viney & Marion Haas & Deborah J. Street, 2011. "Preferences for new and existing contraceptive products," Health Economics, John Wiley & Sons, Ltd., vol. 20(S1), pages 35-52, September.
    3. Randall P. Ellis & Wenjia Zhu, 2016. "Health Plan Type Variations in Spells of Health-Care Treatment," American Journal of Health Economics, University of Chicago Press, vol. 2(4), pages 399-430, Fall.
    4. Doiron, Denise & Fiebig, Denzil G. & Suziedelyte, Agne, 2014. "Hips and hearts: The variation in incentive effects of insurance across hospital procedures," Journal of Health Economics, Elsevier, vol. 37(C), pages 81-97.
    5. Suziedelyte, Agne, 2012. "How does searching for health information on the Internet affect individuals' demand for health care services?," Social Science & Medicine, Elsevier, vol. 75(10), pages 1828-1835.
    6. Nordin, Martin & Dackehag, Margareta & Gerdtham, Ulf-G., 2013. "Socioeconomic inequalities in drug utilization for Sweden: Evidence from linked survey and register data," Social Science & Medicine, Elsevier, vol. 77(C), pages 106-117.
    7. Madeleine T. King & Jane Hall & Emily Lancsar & Denzil Fiebig & Ishrat Hossain & Jordan Louviere & Helen K. Reddel & Christine R. Jenkins, 2007. "Patient preferences for managing asthma: results from a discrete choice experiment," Health Economics, John Wiley & Sons, Ltd., vol. 16(7), pages 703-717, July.
    8. Griliches, Zvi, 1986. "Economic data issues," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 25, pages 1465-1514, Elsevier.
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    Cited by:

    1. Jae H. Kim & In Choi, 2021. "Choosing the Level of Significance: A Decision‐theoretic Approach," Abacus, Accounting Foundation, University of Sydney, vol. 57(1), pages 27-71, March.

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