TY - GEN
T1 - Analyzing healthcare big data for patient satisfaction
AU - Wan, Kaiyu
AU - Alagar, Vangalur
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/21
Y1 - 2018/6/21
N2 - 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.
AB - 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.
KW - Big Data
KW - Health Care Domain
KW - Hospital-based Services
KW - Patient Satisfaction Analysis
KW - e-Health Services
UR - http://www.scopus.com/inward/record.url?scp=85050201344&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2017.8393093
DO - 10.1109/FSKD.2017.8393093
M3 - Conference Proceeding
AN - SCOPUS:85050201344
T3 - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 2084
EP - 2091
BT - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Zhao, Liang
A2 - Wang, Lipo
A2 - Cai, Guoyong
A2 - Li, Kenli
A2 - Liu, Yong
A2 - Xiao, Guoqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Y2 - 29 July 2017 through 31 July 2017
ER -