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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
Authors
Stephan Kudyba  New Jersey Institute of Technology, University Heights, CAB Building, Newark, NJ
G. Brent Hamar  American Healthways, Inc., Nashville, TN
William M. Gandy  American Healthways, Inc., Nashville, TN
Publisher
ACM  New York, NY, USA
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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.

 
1
Cousins, M., Shickle, L., and Bander, J. An introduction to predictive modeling for disease management risk stratification. Disease Management Journal 5 (2002), 157--167.
 
2
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.
 
3
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.
 
4
 
5
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.


Collaborative Colleagues:
Stephan Kudyba: colleagues
G. Brent Hamar: colleagues
William M. Gandy: colleagues