1183 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 71 DOI: 10.4018/978-1-4666-2038-4.ch071 INTRODUCTION The increasing demand for health data analysis in spatial and temporal scale has made emerging tech- nologies such as Geographic Information Systems (GIS) an essential tool for healthcare information systems. In healthcare settings application of such new technology are proving useful in the analysis of health data and planning of healthcare services (Pfeiffer, Robinson, Stevenson, Stevens, Rogers, & Clements, 2008). The ability of GIS to manage and retrieve georeference data has demonstrated its value in the integration of complex epidemio- Joseph M. Woodside Cleveland State University, USA Iftikhar U. Sikder Cleveland State University, USA GIS Application of Healthcare Data for Advancing Epidemiological Studies ABSTRACT Healthcare practices increasingly rely on advanced technologies to improve analysis capabilities for decision making. In particular, spatial epidemiological approach to healthcare studies provides signif- cant insight in evaluating health intervention and decisions through Geographic Information Systems (GIS) applications. This chapter illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization over a period 1994 – 2006. The objective is to fnd excess space and space-time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The fndings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.