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Outlier detection in healthcare fraud: A case study in the Medicaid dental domain

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  • van Capelleveen, Guido
  • Poel, Mannes
  • Mueller, Roland M.
  • Thornton, Dallas
  • van Hillegersberg, Jos

Abstract

Health care insurance fraud is a pressing problem, causing substantial and increasing costs in medical insurance programs. Due to large amounts of claims submitted, estimated at 5 billion per day, review of individual claims or providers is a difficult task. This encourages the employment of automated pre-payment controls and better post-payment decision support tools to enable subject matter expert analysis. This paper presents how to apply unsupervised outlier techniques at post-payment stage to detect fraudulent patterns of received insurance claims. A special emphasis in this paper is put on the system architecture, the metrics designed for outlier detection and the flagging of suspicious providers which may support the fraud experts in evaluating providers and reveal fraud. The algorithms were tested on Medicaid data encompassing 650,000 health-care claims and 369 dentists of one state. Two health care fraud experts evaluated flagged cases and concluded that 12 of the top 17 providers (71%) submitted suspicious claim patterns and should be referred to officials for further investigation. The remaining 5 providers (29%) could be considered mis-classifications as their patterns could be explained by special characteristics of the provider. Selecting top flagged providers is demonstrated to be a valuable as an targeting method, and individual provider analysis revealed some cases of potential fraud. The study concludes that, through outlier detection, new patterns of potential fraud can be identified and possibly utilized in future automated detection mechanisms.

Suggested Citation

  • van Capelleveen, Guido & Poel, Mannes & Mueller, Roland M. & Thornton, Dallas & van Hillegersberg, Jos, 2016. "Outlier detection in healthcare fraud: A case study in the Medicaid dental domain," International Journal of Accounting Information Systems, Elsevier, vol. 21(C), pages 18-31.
    • Handle: RePEc:eee:ijoais:v:21:y:2016:i:c:p:18-31
    DOI: 10.1016/j.accinf.2016.04.001
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    References listed on IDEAS

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    1. Jing Li & Kuei-Ying Huang & Jionghua Jin & Jianjun Shi, 2008. "A survey on statistical methods for health care fraud detection," Health Care Management Science, Springer, vol. 11(3), pages 275-287, September.
    2. Dilla, William N. & Raschke, Robyn L., 2015. "Data visualization for fraud detection: Practice implications and a call for future research," International Journal of Accounting Information Systems, Elsevier, vol. 16(C), pages 1-22.
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    Cited by:

    1. Pei, Duo & Vasarhelyi, Miklos A., 2020. "Big data and algorithmic trading against periodic and tangible asset reporting: The need for U-XBRL," International Journal of Accounting Information Systems, Elsevier, vol. 37(C).
    2. Koreff, Jared & Weisner, Martin & Sutton, Steve G., 2021. "Data analytics (ab) use in healthcare fraud audits," International Journal of Accounting Information Systems, Elsevier, vol. 42(C).
    3. O'Malley, A. James & Bubolz, Thomas A. & Skinner, Jonathan S., 2023. "The diffusion of health care fraud: A bipartite network analysis," Social Science & Medicine, Elsevier, vol. 327(C).
    4. Alyssa J. Rolfe, 2021. "Weighted risk models for dynamic healthcare fraud detection," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(2), pages 143-150, June.
    5. Firuz Kamalov & Ho Hon Leung, 2020. "Outlier Detection in High Dimensional Data," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-16, March.

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