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dc.contributor.advisorCock, Martine D
dc.contributor.authorLiu, Jiacheng
dc.date.accessioned2018-04-24T22:16:43Z
dc.date.submitted2018
dc.identifier.otherLiu_washington_0250O_18363.pdf
dc.identifier.urihttp://hdl.handle.net/1773/41723
dc.descriptionThesis (Master's)--University of Washington, 2018
dc.description.abstractThis thesis aims to develop techniques to help large hospital systems to detect providers with excess spending. Identifying fraud, waste, and abuse resulting in super uous expendi- tures associated with care delivery is central to the success of these large hospital systems and for making the cost of healthcare sustainable. In theory, such expenditures should be easily identiable with large amounts of historical data. However, to the best of our knowledge there is no data mining framework that systematically addresses the problem of identifying unwarranted variation in expenditures on high dimensional claims data using unsupervised machine learning techniques. In this thesis, we propose methods to uncover unwarranted variation in healthcare spending by automatically extracting reference groups of peer-providers from the data and then detecting high cost outliers within these groups. Besides we also implement existing graph based techniques and compare the results with our methods. We demonstrate the utility of our proposed framework on datasets from a large ACO (Accountable Care Organization) in the Pacic Northwest of the United States to successfully identify unwarranted variation in the provision of therapeutic procedures that had previously gone unnoticed.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsnone
dc.subjectClustering
dc.subjectHealthcare Data Mining
dc.subjectOutlier Detection
dc.subjectComputer science
dc.subject.otherTo Be Assigned
dc.titleAutomatic Detection of Providers with Excess Healthcare Spending
dc.typeThesis
dc.embargo.termsRestrict to UW for 1 year -- then make Open Access
dc.embargo.lift2019-04-24T22:16:43Z


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