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Informatics Application Challenges for Managed Care Organizations: The Three Faces of Population Segmentation and a Proposed Classification System

Informatics Application Challenges for Managed Care Organizations: The Three Faces of Population Segmentation and a Proposed Classification System

Stephan Kudyba, Theodore L. Perry
Copyright: © 2008 |Volume: 3 |Issue: 2 |Pages: 11
ISSN: 1555-3396|EISSN: 1555-340X|ISSN: 1555-3396|EISBN13: 9781615202935|EISSN: 1555-340X|DOI: 10.4018/jhisi.2008040103
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MLA

Kudyba, Stephan, and Theodore L. Perry. "Informatics Application Challenges for Managed Care Organizations: The Three Faces of Population Segmentation and a Proposed Classification System." IJHISI vol.3, no.2 2008: pp.21-31. http://doi.org/10.4018/jhisi.2008040103

APA

Kudyba, S. & Perry, T. L. (2008). Informatics Application Challenges for Managed Care Organizations: The Three Faces of Population Segmentation and a Proposed Classification System. International Journal of Healthcare Information Systems and Informatics (IJHISI), 3(2), 21-31. http://doi.org/10.4018/jhisi.2008040103

Chicago

Kudyba, Stephan, and Theodore L. Perry. "Informatics Application Challenges for Managed Care Organizations: The Three Faces of Population Segmentation and a Proposed Classification System," International Journal of Healthcare Information Systems and Informatics (IJHISI) 3, no.2: 21-31. http://doi.org/10.4018/jhisi.2008040103

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Abstract

Organizations across industry sectors continue to develop data resources and utilize analytic techniques to enhance efficiencies in their operations. One example of this is evident as Managed Care Organizations (MCOs) enhance their care and disease management initiatives through the utilization of population segmentation techniques. This article proposes a classification system for population segmentation techniques for care and disease management and provides an evaluation process for each. The three proposed operational areas for Managed Care Organizations are: 1) Risk Status: early identification of high-risk patients, 2) Treatment Status: compliance with treatment protocols, and 3) Health Status: severity of illness or episodes of care groupings, all of which require particular analytic methodologies to leverage data resources. By applying this classification system an MCO can improve its ability to clarify internal goals for population segmentation, more accurately apply existing analytic methodologies, and produce more appropriate solutions.

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