A conceptual framework for quality healthcare accessibility: a scalable approach for big data technologies
- 509 Downloads
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.
KeywordsGeographic information systems Big data analytics Healthcare analytics Healthcare accessibility Big data for decision support
- Aday, L. A., & Andersen, R. (1974). A framework for the study of access to medical care. Health Services Research, 9(3), 208.Google Scholar
- Canlas, R. (2009). Data mining in healthcare: Current applications and issues. School of Information Systems & Management: Carnegie Mellon University, Australia.Google Scholar
- 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
- ESRI (2016a). OD cost matrix analysis. http://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/od-cost-matrix.htm. Accessed 18 Aug 2016.
- ESRI (2016b). Optimized hot spot analysis. http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/optimized-hot-spot-analysis.htm. Accessed 20 Aug 2016.
- 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.
- Hevner, A., & Chatterjee, S. (2010). Design research in information systems: theory and practice (Vol. 22): Springer.Google Scholar
- 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
- Shah, N., & Pathak, J. (2014). Why health care may finally Be ready for big data. Harvard Business Review.Google Scholar
- Vo, A., Plachkinova, M., & Bhaskar, R. (2015). Assessing healthcare accessibility algorithms: A comprehensive investigation of two-step floating catchment methodologies family.Google Scholar