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

Miloslava Plachkinova (), Au Vo (), Rahul Bhaskar () and Brian Hilton ()
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Miloslava Plachkinova: University of Tampa
Au Vo: Claremont Graduate University
Rahul Bhaskar: California State University Fullerton
Brian Hilton: Claremont Graduate University

Information Systems Frontiers, 2018, vol. 20, issue 2, No 8, 289-302

Abstract: 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.

Keywords: Geographic information systems; Big data analytics; Healthcare analytics; Healthcare accessibility; Big data for decision support (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10796-016-9726-y

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