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Detecting hospital fraud and claim abuse through diabetic outpatient services

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  • Fen-May Liou
  • Ying-Chan Tang
  • Jean-Yi Chen

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  • Fen-May Liou & Ying-Chan Tang & Jean-Yi Chen, 2008. "Detecting hospital fraud and claim abuse through diabetic outpatient services," Health Care Management Science, Springer, vol. 11(4), pages 353-358, December.
    • Handle: RePEc:kap:hcarem:v:11:y:2008:i:4:p:353-358
    DOI: 10.1007/s10729-008-9054-y
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    References listed on IDEAS

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    1. Dasgupta, Chanda Ghose & Dispensa, Gary S. & Ghose, Sanjoy, 1994. "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Elsevier, vol. 10(2), pages 235-244, September.
    2. McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
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    Cited by:

    1. Carapinha, João L. & Ross-Degnan, Dennis & Desta, Abayneh Tamer & Wagner, Anita K., 2011. "Health insurance systems in five Sub-Saharan African countries: Medicine benefits and data for decision making," Health Policy, Elsevier, vol. 99(3), pages 193-202, March.
    2. Arash Rashidian & Hossein Joudaki & Taryn Vian, 2012. "No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    3. Farbmacher, Helmut & Löw, Leander & Spindler, Martin, 2022. "An explainable attention network for fraud detection in claims management," Journal of Econometrics, Elsevier, vol. 228(2), pages 244-258.
    4. Conghai Zhang & Xinyao Xiao & Chao Wu, 2020. "Medical Fraud and Abuse Detection System Based on Machine Learning," IJERPH, MDPI, vol. 17(19), pages 1-11, October.

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