Identification and prediction of chronic conditions for health plan members using data mining techniques

Theodore L. Perry, Stephan Kudyba, Kenneth Lawrence

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Data mining techniques have become an essential management tool in today's health care arena. Rising health care costs, the increasing prevalence rates of chronic conditions, and an aging population all add to the complexity and challenges facing our health care delivery systems as we move Into the 21st century [2]. Health care spending in the United States was over $2.1 trillion dollars in 2006, and it is estimated that health care expenditures will represent nearly 20 percent of all U.S. spending by 2016 [3]. Major changes must occur over the next decade in the way we manage our health care resources to meet this need.

Original languageEnglish (US)
Title of host publicationData Mining Methods and Applications
PublisherCRC Press
Pages175-182
Number of pages8
ISBN (Electronic)9781420013733
ISBN (Print)9780849385223
DOIs
StatePublished - Jan 1 2007

Fingerprint

Health care
Data mining
Health
Chronic conditions
Health plans
Prediction
Healthcare
Aging of materials
Costs

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)
  • Computer Science(all)

Cite this

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Identification and prediction of chronic conditions for health plan members using data mining techniques. / Perry, Theodore L.; Kudyba, Stephan; Lawrence, Kenneth.

Data Mining Methods and Applications. CRC Press, 2007. p. 175-182.

Research output: Chapter in Book/Report/Conference proceedingChapter

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