Advertisement

Information Systems Frontiers

, Volume 20, Issue 2, pp 289–302 | Cite as

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

  • Miloslava Plachkinova
  • Au Vo
  • Rahul Bhaskar
  • Brian Hilton
Article

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 

References

  1. Aday, L. A., & Andersen, R. (1974). A framework for the study of access to medical care. Health Services Research, 9(3), 208.Google Scholar
  2. Barrett, M. A., Humblet, O., Hiatt, R. A., & Adler, N. E. (2013). Big data and disease prevention: from quantified self to quantified communities. Big Data, 1(3), 168–175.CrossRefGoogle Scholar
  3. Canlas, R. (2009). Data mining in healthcare: Current applications and issues. School of Information Systems & Management: Carnegie Mellon University, Australia.Google Scholar
  4. d’Agostino, R. (1971). An omnibus test of normality for moderate and large size samples. Biometrika, 58(2), 341–348.CrossRefGoogle Scholar
  5. d’Agostino, R., & Pearson, E. S. (1973). Tests for departure from normality. Empirical results for the distributions of b2 and√b1. Biometrika, 60(3), 613–622.Google Scholar
  6. Delamater, P. L. (2013). Spatial accessibility in suboptimally configured health care systems: a modified two-step floating catchment area (M2SFCA) metric. Health & Place, 24, 30–43.CrossRefGoogle Scholar
  7. Dony, C. C., Delmelle, E. M., & Delmelle, E. C. (2015). Re-conceptualizing accessibility to parks in multi-modal cities: a variable-width floating catchment area (VFCA) method. Landscape and Urban Planning, 143, 90–99.CrossRefGoogle Scholar
  8. ESRI (2016a). OD cost matrix analysis. http://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/od-cost-matrix.htm. Accessed 18 Aug 2016.
  9. Fransen, K., Neutens, T., De Maeyer, P., & Deruyter, G. (2015). A commuter-based two-step floating catchment area method for measuring spatial accessibility of daycare centers. Health & Place, 32, 65–73.CrossRefGoogle Scholar
  10. Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255–261.CrossRefGoogle Scholar
  11. Groves, P., Kayyali, B., Knott, D., & Van Kuiken, S. (2013). The ‘big data’ revolution in healthcare. http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care. Accessed 16 Nov 2016.
  12. Guagliardo, M. F. (2004). Spatial accessibility of primary care: concepts, methods and challenges. International Journal of Health Geographics, 3(1), 3.CrossRefGoogle Scholar
  13. Hevner, A., & Chatterjee, S. (2010). Design research in information systems: theory and practice (Vol. 22): Springer.Google Scholar
  14. Hevner, A., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.CrossRefGoogle Scholar
  15. Higgs, G. (2004). A literature review of the use of GIS-based measures of access to health care services. Health Services & Outcomes Research Methodology, 5(2), 119–139.CrossRefGoogle Scholar
  16. Jee, K., & Kim, G.-H. (2013). Potentiality of big data in the medical sector: focus on how to reshape the healthcare system. Healthcare informatics research, 19(2), 79–85.CrossRefGoogle Scholar
  17. Khan, A. A. (1992). An integrated approach to measuring potential spatial access to health care services. Socio-Economic Planning Sciences, 26(4), 275–287.CrossRefGoogle Scholar
  18. Langford, M., Higgs, G., & Fry, R. (2016). Multi-modal two-step floating catchment area analysis of primary health care accessibility. Health & Place, 38, 70–81.CrossRefGoogle Scholar
  19. Luo, J. (2014). Integrating the huff model and floating catchment area methods to analyze spatial access to healthcare services. Transactions in GIS, 18(3), 436–448.CrossRefGoogle Scholar
  20. Luo, W., & Qi, Y. (2009). An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health & Place, 15(4), 1100–1107.CrossRefGoogle Scholar
  21. Luo, W., & Wang, F. (2003). Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environment and Planning. B, Planning & Design, 30, 865–884.CrossRefGoogle Scholar
  22. Luo, W., & Whippo, T. (2012). Variable catchment sizes for the two-step floating catchment area (2SFCA) method. Health & Place, 18(4), 789–795.CrossRefGoogle Scholar
  23. Mao, L., & Nekorchuk, D. (2013). Measuring spatial accessibility to healthcare for populations with multiple transportation modes. Health & Place, 24, 115–122.CrossRefGoogle Scholar
  24. McGrail, M. R., & Humphreys, J. S. (2014). Measuring spatial accessibility to primary health care services: Utilising dynamic catchment sizes. Applied Geography, 54, 182–188.CrossRefGoogle Scholar
  25. Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. JAMA, 309(13), 1351–1352.CrossRefGoogle Scholar
  26. Musa, G. J., Chiang, P.-H., Sylk, T., Bavley, R., Keating, W., Lakew, B., et al. (2013). Use of GIS mapping as a public health tool—from cholera to cancer. Health Services Insights, 6, 111.CrossRefGoogle Scholar
  27. Ngui, A. N., & Apparicio, P. (2011). Optimizing the two-step floating catchment area method for measuring spatial accessibility to medical clinics in Montreal. BMC Health Services Research, 11(1), 166.CrossRefGoogle Scholar
  28. Polzin, P., Borges, J., & Coelho, A. (2014). An extended kernel density two-step floating catchment area method to analyze access to health care. Environment and Planning. B, Planning & Design, 41(4), 717–735.CrossRefGoogle Scholar
  29. Radke, J., & Mu, L. (2000). Spatial decompositions, modeling and mapping service regions to predict access to social programs. Geographic Information Sciences, 6(2), 105–112.Google Scholar
  30. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.CrossRefGoogle Scholar
  31. Robinson, M., & Clegg, J. (2005). Improved determination of Q-factor and resonant frequency by a quadratic curve-fitting method. IEEE Transactions on Electromagnetic Compatibility, 47(2), 399–402.CrossRefGoogle Scholar
  32. Shah, N., & Pathak, J. (2014). Why health care may finally Be ready for big data. Harvard Business Review.Google Scholar
  33. Siegel, M., Koller, D., Vogt, V., & Sundmacher, L. (2016). Developing a composite index of spatial accessibility across different health care sectors: a German example. Health Policy, 120(2), 205–212.CrossRefGoogle Scholar
  34. Soliman, S., Christensen, G., & Rouhi, A. (1988). A new technique for curve fitting based on minimum absolute deviations. Computational Statistics and Data Analysis, 6(4), 341–351.CrossRefGoogle Scholar
  35. Vo, A., Plachkinova, M., & Bhaskar, R. (2015). Assessing healthcare accessibility algorithms: A comprehensive investigation of two-step floating catchment methodologies family.Google Scholar
  36. Wan, N., Zou, B., & Sternberg, T. (2012). A three-step floating catchment area method for analyzing spatial access to health services. International Journal of Geographical Information Science, 26(6), 1073–1089.CrossRefGoogle Scholar
  37. Wang, F., & Luo, W. (2005). Assessing spatial and nonspatial factors for healthcare access: towards an integrated approach to defining health professional shortage areas. Health & Place, 11(2), 131–146.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Miloslava Plachkinova
    • 1
  • Au Vo
    • 2
  • Rahul Bhaskar
    • 3
  • Brian Hilton
    • 2
  1. 1.Department of Information and Technology Management, Sykes College of BusinessUniversity of TampaTampaUSA
  2. 2.Center for Information Systems and TechnologyClaremont Graduate UniversityClaremontUSA
  3. 3.Department of Information Systems and Decision Sciences, Mihaylo College of Business and EconomicsCalifornia State University FullertonFullertonUSA

Personalised recommendations