2014
Silva, E.; Alpuim, A.; Cardoso, L.; Marins, F.; Quintas, C.; Portela, C. F.; Santos, M. F.; Machado, J.; Abelha, A.
Business intelligence and nosocomial infection decision making Book Chapter
Em: pp. 193-215, IGI Global, 2014, ISBN: 9781466664784; 1466664770; 9781466664777, (cited By 3).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Decision support systems; Health care; Information analysis; Information management, Clinical decision support systems; Data manipulations; Data mining models; Decision making process; Health care professionals; Healthcare environments; Healthcare organizations; Nosocomial infection, Decision making
@inbook{Silva2014193,
title = {Business intelligence and nosocomial infection decision making},
author = {E. Silva and A. Alpuim and L. Cardoso and F. Marins and C. Quintas and C. F. Portela and M. F. Santos and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946131048&doi=10.4018%2f978-1-4666-6477-7.ch010&partnerID=40&md5=6b84b26387401612ee03872a886f1404},
doi = {10.4018/978-1-4666-6477-7.ch010},
isbn = {9781466664784; 1466664770; 9781466664777},
year = {2014},
date = {2014-01-01},
journal = {Integration of Data Mining in Business Intelligence Systems},
pages = {193-215},
publisher = {IGI Global},
abstract = {The implementation of Business Intelligence tools in healthcare organizations helps the managers and the healthcare professionals in their decision making process through data manipulation and data analysis. The main goal of this chapter is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). The knowledge obtained with the analysis of the indicators and the knowledge obtained with the nosocomial infection prediction can be applied by healthcare professionals in their decision making. Through the analysis of the data collected, Business Intelligence tools help overcome the problems associated with the complexity, heterogeneity, and distributiveness present in the healthcare environment. © 2015, IGI Global.},
note = {cited By 3},
keywords = {Artificial intelligence; Decision support systems; Health care; Information analysis; Information management, Clinical decision support systems; Data manipulations; Data mining models; Decision making process; Health care professionals; Healthcare environments; Healthcare organizations; Nosocomial infection, Decision making},
pubstate = {published},
tppubtype = {inbook}
}
The implementation of Business Intelligence tools in healthcare organizations helps the managers and the healthcare professionals in their decision making process through data manipulation and data analysis. The main goal of this chapter is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). The knowledge obtained with the analysis of the indicators and the knowledge obtained with the nosocomial infection prediction can be applied by healthcare professionals in their decision making. Through the analysis of the data collected, Business Intelligence tools help overcome the problems associated with the complexity, heterogeneity, and distributiveness present in the healthcare environment. © 2015, IGI Global.