Evaluating the Impact of Universal Health Coverage Policies on Population Health Outcomes Using Big Data Analytics for Comprehensive Policy Assessment

Abstract

Universal Health Coverage (UHC) aims to eliminate financial barriers to care which is important for everyone’s affordability. Thus, it is helpful to study how UHC affects population health outcomes and where it may be headed in the future. Big Data Analytics provides good tools for scrutinizing those principles and their results on a huge scale. Communities differ in terms of their health outcomes; and health systems are complex. This paper tries to address these among other issues by integrating big data sets that are diverse and analysing them together. As an innovative way of examining universal health coverage initiatives’ impact on populations, researchers propose the Comprehensive Analysis of Fragmented Health Systems (CA-FHS). CA-FHS uses Big Data Analytics to compile and analyse information from many different sources thereby giving an exhaustive breakdown of health outcomes by demography as well as geographic area. This would allow trends or patterns not seen using conventional evaluation tools to be discovered. It encompasses public health, policy-making, as well as healthcare management concerns. In this way, the method may bring out the strengths and weaknesses inherent in the healthcare system such that policies are recommended for change while resource allocation is done for bettering UHC-related consequences internationally. It will enable the evaluation of long-term effects resulting from these projects so that they can meet their goals eventually. The process will involve creating hypothetical policy scenarios and then assessing how these would affect population’s historical health outcomes through use of historical data simulation techniques; thus providing input into potential policy choices at federal level concerning evidence-based recommendations towards achieving improvement in health status.