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The Application of Data Mining Techniques in Health Plan Population Management: A Disease Management Approach
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Author(s): Theodore L. Perry (American Healthways Corp., USA), Travis Tucker (American Healthways Corp., USA), Laurel R. Hudson (American Healthways Corp., USA), William Gandy (American Healthways Corp., USA), Amy L. Neftzger (American Healthways Corp., USA)and Guy B. Hamar (American Healthways Corp., USA)
Copyright: 2004
Pages: 21
Source title:
Managing Data Mining: Advice from Experts
Source Author(s)/Editor(s): Stephan Kudyba (New Jersey Institute of Technology, USA)
DOI: 10.4018/978-1-59140-243-5.ch007
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Abstract
Healthcare has become a data-intensive business. Over the last 30 years, we have seen significant advancements in the areas of health information technology and health informatics as well as healthcare modeling and artificial intelligence techniques. Health informatics, which is the science of health information,1 has made great progress during this period (American Medical Informatics Association). Likewise, data mining, which has been generally defined as the application of technology and statistical/mathematical methods to uncover relationships and patterns between variables in data sets, has experienced noteworthy improvements in computer technology (e.g., hardware and software) in addition to applications and methodologies (e.g., statistical and biostatistical techniques such as neural networks, regression analysis, and classification/segmentation methods) (Kudyba & Hoptroff, 2001). Though health informatics is a relatively young science, the impact of this area on the health system and health information technology industry has already been seen, evidenced by improvements in healthcare delivery models, information systems, and assessment/diagnostic tools.
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