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Author Kudyba, Stephan ♦ Hamar, G. Brent ♦ Gandy, William M.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF ♦ HTM / HTML
Language English
Abstract This paper illustrates the use of advanced analytics to increase efficiency in the healthcare sector through cost reduction. The application of multivariate techniques on health population data depicted better accuracy in identifying patients at risk of developing a chronic illness (diabetes) than more conventional techniques. The model results enable healthcare providers to more effectively apply preventive treatment methods to the at-risk population to reduce the likelihood of individuals from experiencing a fully developed illness. An estimate of the cost savings in the form of preventing cases of fully developed diabetes through predictive modeling is included.
Description Affiliation: New Jersey Institute of Technology, University Heights, CAB Building, Newark, NJ (Kudyba, Stephan) || American Healthways, Inc., Nashville, TN (Hamar, G. Brent; Gandy, William M.)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 48
Issue Number 12
Page Count 4
Starting Page 107
Ending Page 110


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Source: ACM Digital Library