Abstract
This study has as its main objective the analysis of healthcare claims data from individual providers, such as independent doctors and allied health professionals, with the purpose of finding excessive billing of medical visitation procedures. We present a discussion of the main difficulties in preventing against abusive claims, and with the use of the CRISP-DM method and the k-means clustering algorithm, propose a model for assessing the behavior of providers engaged in this sort of practice. We conclude that the clustering algorithm was able to provide a more efficient, objective, and reproducible framework for identifying outliers, which could be used for future investigations in similar datasets.
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Notes
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http://www.pwc.com.br/pt/publicacoes/setores-atividade/assets/saude/healthcare-tsp-13.pdf. Accessed 11-Dec-2014.
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Research focused on the IEEE Xplore Digital Library (ieeexplore.ieee.org), ScienceDirect (www.sciencedirect.com) Google Scholar (scholar.google.com), SpringerLink (http://link.springer.com) and Elsevier (http://www.elsevier.com/about/open-access/sponsored-articles). Keywords included impossible day, healthcare billing, excessive medical claims, healthcare fraud and abuse, cluster analysis etc.
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RStudio is a widely used development environment for R, available at http://www.rstudio.com/products/RStudio/.
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Hillerman, T.P., Carvalho, R.N., Reis, A.C.B. (2015). Analyzing Suspicious Medical Visit Claims from Individual Healthcare Service Providers Using K-Means Clustering. In: Kő, A., Francesconi, E. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2015. Lecture Notes in Computer Science, vol 9265. Springer, Cham. https://doi.org/10.1007/978-3-319-22389-6_14
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