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A conceptual framework for quality healthcare accessibility: a scalable approach for big data technologies

Author

Listed:
  • Miloslava Plachkinova

    (University of Tampa)

  • Au Vo

    (Claremont Graduate University)

  • Rahul Bhaskar

    (California State University Fullerton)

  • Brian Hilton

    (Claremont Graduate University)

Abstract

Healthcare accessibility research has been of growing interest for scholars and practitioners. This manuscript classifies prior studies on the Floating Catchment Area methodologies, a prevalent class of methodologies that measure healthcare accessibility, and presents a framework that conceptualizes accessibility computation. We build the Floating Catchment Method General Framework as an IT artifact, following best practices in Design Science Research. We evaluate the utility of our framework by creating an instantiation, as an algorithm, and test it with large healthcare data sets from California. We showcase the practical application of the artifact and address the pressing issue of access to quality healthcare. This example also serves as a prototype for Big Data Analytics, as it presents opportunities to scale the analysis vertically and horizontally. In order for researchers to perform high impact studies and make the world a better place, an overarching framework utilizing Big Data Analytics should be seriously considered.

Suggested Citation

  • Miloslava Plachkinova & Au Vo & Rahul Bhaskar & Brian Hilton, 0. "A conceptual framework for quality healthcare accessibility: a scalable approach for big data technologies," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9726-y
    DOI: 10.1007/s10796-016-9726-y
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    References listed on IDEAS

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    1. Soliman, S. A. & Christensen, G. S. & Rouhi, A., 1988. "A new technique for curve fitting based on minimum absolute deviations," Computational Statistics & Data Analysis, Elsevier, vol. 6(4), pages 341-351, June.
    2. Pierre Polzin & José Borges & António Coelho, 2014. "An Extended Kernel Density Two-Step Floating Catchment Area Method to Analyze Access to Health Care," Environment and Planning B, , vol. 41(4), pages 717-735, August.
    3. Khan, Abdullah A., 1992. "An integrated approach to measuring potential spatial access to health care services," Socio-Economic Planning Sciences, Elsevier, vol. 26(4), pages 275-287, October.
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    Cited by:

    1. Xinyue Zhang & Xiaolu Gao & Danxian Wu & Zening Xu & Hongjie Wang, 2021. "The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework," Sustainability, MDPI, vol. 13(21), pages 1-19, October.
    2. Shivam Gupta & Vinayak A. Drave & Surajit Bag & Zongwei Luo, 2019. "Leveraging Smart Supply Chain and Information System Agility for Supply Chain Flexibility," Information Systems Frontiers, Springer, vol. 21(3), pages 547-564, June.
    3. Ashish Gupta & Amit Deokar & Lakshmi Iyer & Ramesh Sharda & Dave Schrader, 2018. "Big Data & Analytics for Societal Impact: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(2), pages 185-194, April.
    4. Paul L. Delamater, 2018. "Comment on “A Conceptual Framework for Quality Healthcare Accessibility: a Scalable Approach for Big Data Technologies”," Information Systems Frontiers, Springer, vol. 20(2), pages 303-309, April.
    5. Miloslava Plachkinova & Au Vo & Brian Hilton & Rahul Bhaskar, 2018. "Response to Delamater’s Comment on “A Conceptual Framework for Quality Healthcare Accessibility: A Scalable Approach for Big Data Technologies”," Information Systems Frontiers, Springer, vol. 20(2), pages 311-314, April.

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