Utilization of data- mining techniques for evaluation of patterns of asthma drugs use by ambulatory patients in a large health maintenance organization

Mark Last, Rafael Carel, Dotan Barak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

A major problem of drugs utilization is to identify outlier patients who are using large quantities of drugs over extended periods of time. Today, healthcare and health insurance systems have to deal with an increased number of patients suffering from chronic diseases, such as asthma, who are continuously using a combination of several medications. This has caused a substantial increase in the cost of providing healthcare for such patients. In Israel, 11% of the national health care budget is spent on medications. However, healthcare management operations do not have the information that can assist in determining whether extensive multi-year drug utilization by a chronic patient is an outlier or misuse of resources. In this work, we construct a prediction model for asthma drug utilization by applying novel methods of knowledge discovery in time-series databases to a multi-year asthma drug utilization data set. Methods of mining utilization patterns combine clustering algorithms, clustering validity measures, and decision-tree classification algorithms. This methodology is applied to a regional patients' database maintained in 'Clalit Health Services' HMO, Beer-Sheva, Israel between January 2000 and November 2002. The clustering results reveal that 274 asthma patients who received 9,319 prescriptions during that period can be partitioned into three groups of utilization patterns, where ten patients (3.6%) who used 1,333 prescriptions (14.3%) are classified as outliers. The classification results show that the use of corticosteroids medications (oral or by inhalation) and the age of a patient can be considered as the main predictive factors in the induced models.

Original languageEnglish
Title of host publicationICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages169-174
Number of pages6
DOIs
StatePublished - 1 Dec 2007
Event17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States
Duration: 28 Oct 200731 Oct 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Country/TerritoryUnited States
CityOmaha, NE
Period28/10/0731/10/07

ASJC Scopus subject areas

  • Engineering (all)

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