Analyzing healthcare big data for patient satisfaction

Healthcare Big Data (HBD) is more complex than Big Data (BD) arising from any other critical sector because a variety of data sources and procedures are followed in traditional hospital settings and in healthcare network (e-Health). In order to achieve their primary goal, which is to enhance patient experience while sustaining dependable care within financial viability and respect for government regulations, the HBD should be analyzed to determine patent satisfaction level. In general, there exists no accepted method yet in measuring patient satisfaction. The traditional approach for evaluating hospital-based healthcare is through a statistical analysis of responses of clients to a survey, often conducted by a third party. Such methods are often infected with incomplete information, inaccurate hypothesis, and error-prone analysis. Analyzing data generated through automated healthcare networks for assessing the effectiveness of service provision and patient satisfaction are more challenging. It is in this context that we discuss in this paper factors that contribute to patient satisfaction, and propose an algorithmic method to assess it from HBD analysis.