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Mining Medical Specialist Billing Patterns for Health Service Management
Shan, Y., Jeacocke, D., Murray, D.W. and Sutinen, A.
This paper presents an application of association rule
mining in compliance in the context of health service
management. There are approximately 500 million
transactions processed by Medicare Australia each year.
Among these transactions, there exist a small proportion
of suspicious claims. This study applied association rule
mining to examine billing patterns within a particular
specialist group to detect these suspicious claims and
potential fraudulent individuals. This work identified both
positive and negative association rules from specialist
billing records. All the rules identified were examined by
a subject matter expert, a practicing clinician, to classify
them into two groups, those representing compliant
patterns and non-compliant patterns. The rules
representing compliant patterns were then used to detect
potentially fraudulent claims by examining whether
claims are consistent with these rules. The individuals
whose claims frequently break these rules are identified as
potentially high risk. Due to the difficulty of direct
assessment on high risk individuals, the relevance of this
method to fraud detection is validated by analysis of the
individual specialist's compliance history. The results
clearly demonstrate that association rule mining is an
effective method of identifying suspicious billing patterns
by medical specialists and therefore is a valuable tool in
fraud detection for health service management. |
Cite as: Shan, Y., Jeacocke, D., Murray, D.W. and Sutinen, A. (2008). Mining Medical Specialist Billing Patterns for Health Service Management. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick, J. F., Li, J., Christen, P. and Kennedy, P. J., Eds. ACS. 105-110. |
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