| Enhancing efficiency in the health care industry |
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Communications of the ACM
archive
Volume 48 , Issue 12 (December 2005)
table of contents
The semantic e-business vision
Pages: 107 - 110
Year of Publication: 2005
ISSN:0001-0782
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Authors
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Stephan Kudyba
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New Jersey Institute of Technology, University Heights, CAB Building, Newark, NJ
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G. Brent Hamar
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American Healthways, Inc., Nashville, TN
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William M. Gandy
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American Healthways, Inc., Nashville, TN
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Downloads (6 Weeks): 8, Downloads (12 Months): 97, Citation Count: 1
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ABSTRACT
Using critical data and predictive modeling can help identify high-risk candidates in a total health plan population.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Cousins, M., Shickle, L., and Bander, J. An introduction to predictive modeling for disease management risk stratification. Disease Management Journal 5 (2002), 157--167.
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DPP Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine 346, 6, (2002), 393--403.
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Grana, J., Preston, S., McDermott, P.D., and Hanchak, H.A. The use of administrative data to risk-stratify asthmatic patients. American Journal of Medical Quality 12, (2002), 113--119.
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Kiernan, M., Kraemer, H., Winkleby, M., King, A., and Taylor, C. Do logistic regression and signal detection identify different subgroups at risk?: Implications for the design of tailored interventions. Journal of Philosophy, Psychology and Scientific Methods 6, (2001), 35--48.
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Shelton, P. Disease management programs: The second generation. Disease Management and Health Outcomes 10, 8 (2002), 461--467.
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