514
Views
9
CrossRef citations to date
0
Altmetric
Statistical Practice

On the Use of the Concentration Function in Medical Fraud Assessment

, ORCID Icon, &
Pages 236-241 | Received 01 Aug 2015, Published online: 18 Oct 2017

References

  • Cifarelli, D. M., and Regazzini, E. (1987), “On a General Definition of Concentration Function,” Sankhyā B, 49, 307–319.
  • Edwards, D., Ward-Besser, G., Lasecki, J., Parker, B., Wieduwilt, K., Wu, F., and Moorhead, P. (2003), “The Minimum Sum Method: A Distribution-Free Sampling Procedure for Medicare Fraud Investigations,” Health Services and Outcomes Research Methodology, 4, 241–263.
  • Ekin, T., Ieva, F., Ruggeri, F., and Soyer, R. (2013), “Application of Bayesian Methods in Detection of Healthcare Fraud,” Chemical Engineering Transactions, 33, 151–156.
  • Ekin, T., Musal, R. M., and Fulton, L. V. (2015), “Overpayment Models for Medical Audits: Multiple Scenarios,” Journal of Applied Statistics, 42, 2391–2405.
  • Gilliland, D., and Edwards, D. (2011), “Using Randomized Confidence Limits to Balance Risk: An Application to Medicare Investigations,” The American Statistician, 65, 149–153.
  • Gini, C. (1914), “Sulla Misura Della Concentrazione Della Variabilità Dei Caratteri,” Atti del Reale Istituto Veneto di S.L.A., A.A. 1913-1914, 73, 1203–1248.
  • Ignatova, I., Deutsch, R. C., and Edwards, D. (2012), “Closed Sequential and Multistage Inference on Binary Responses With or Without Replacement,” The American Statistician, 66, 163–172.
  • Laleh, N., and Azgomi, M. A. (2009), “A Taxonomy of Frauds and Fraud Detection Techniques,” in Information Systems, Technology and Management: ICISTM 2009, eds. S. K. Prasad, S. Routray, R. Khurana, and S. Sahni, Berlin: Springer, pp. 256–267.
  • Li, J., Huang, K-Y., Jin, J., and Shi, J. (2008), “A Survey on Statistical Methods for Health Care Fraud Detection,” Health Care Management Science, 11, 275–287.
  • Lu, F., and Boritz, J. E. (2005), “Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions,” in Machine Learning: ECML 2005, eds. J. Gama, R. Camacho, P. B. Brazdil, A. M. Jorge, and L. Torgo, Berlin: Springer, pp. 633–640.
  • Marshall, A. W., and Olkin, I. (1979), Inequalities: Theory of Majorization and its Applications, New York: Academic Press.
  • Musal, R. M. (2010), “Two Models to Investigate Medicare Fraud Within Unsupervised Databases,” Expert Systems with Applications, 37, 8628–8633.
  • Musal, R. M., and Ekin, T. (2017), “Medical Overpayment Estimation: A Bayesian Approach,” Statistical Modelling, 17, 196–222.
  • Ng, K. S., Shan, Y., Murray, D. W., Sutinen, A., Schwarz, B., Jeacocke, D., and Farrugia, J. (2010), “Detecting Non-Compliant Consumers in Spatio-Temporal Health Data: A Case Study From Medicare Australia,” in 2010 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 613–622.
  • Onderwater, M. (2010), Detecting Unusual User Profiles With Outlier Detection Techniques, Amsterdam: VU University.
  • Pietra, G. (1915), “Delle Relazioni tra gli Indici di Variabilità,” Atti del Reale Istituto Veneto di S.L.A. A.A. 1914-1915, 74, 775–792.
  • Woodard, B. (2015), “Fighting Healthcare Fraud With Statistics,” Significance, 12, 22–25.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.