Non-Traditional Data Mining Applications in Taiwan National Health Insurance (NHI) Databases: A Hybrid Mining (HM) Case for the Framing of NHI Decisions

Joseph Tan and Fuchung Wang
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Joseph Tan: DeGroote School of Business,McMaster University, Hamilton, Canada
Fuchung Wang: National Health Insurance Administration, Taipei City, Taiwan

International Journal of Healthcare Information Systems and Informatics (IJHISI), 2017, vol. 12, issue 4, 31-51

Abstract: This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance (NHI) databases. In order to obtain the best payment management, a hybrid mining (HM) approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytic processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will assist in directing the health insurance decision-making process, is built. Drawing from lessons learned within a case study setting, results showed that not only is HM approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Essentially, HM approach can provide a critical boost to health insurance decision support; hence, future researchers should develop and improve the approach combined with their own application systems.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jhisi0:v:12:y:2017:i:4:p:31-51

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