Paper Info
Title
Analyzing Healthcare Big Data For Patient Satisfaction
Abstract
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.
Year
Venue
Keywords
2017
2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD)
Big Data, Health Care Domain, Hospital-based Services, e-Health Services, Patient Satisfaction Analysis
Field
DocType
Citations 
Health care,Service provision,Computer science,Patient satisfaction,Artificial intelligence,Patient experience,Big data,Machine learning,Complete information,Operations management,Government,Statistical analysis
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Kaiyu Wan1198.17
Vangalur S. Alagar216439.10