Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    11 Citations (Scopus)
    462 Downloads (Pure)

    Abstract

    Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.
    Original languageUndefined
    Title of host publicationProceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014)
    PublisherSCITEPRESS
    Pages684-694
    Number of pages11
    ISBN (Print)978-989-758-028-4
    DOIs
    Publication statusPublished - Apr 2014
    Event16th International Conference on Enterprise Information Systems, ICEIS 2014 - Lisbon, Portugal
    Duration: 27 Apr 201430 Apr 2014
    Conference number: 16

    Publication series

    Name
    PublisherScitepress

    Conference

    Conference16th International Conference on Enterprise Information Systems, ICEIS 2014
    Abbreviated titleICEIS
    CountryPortugal
    CityLisbon
    Period27/04/1430/04/14

    Keywords

    • EWI-24984
    • Fraud detection
    • Medicaid
    • Healthcare Fraud
    • METIS-305987
    • IR-91956
    • Anomaly Detection
    • Data Mining
    • Outlier Detection

    Cite this

    Thornton, D., Poel, M., van Hillegersberg, J., Mueller, R., & van Capelleveen, G. C. (2014). Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data. In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014) (pp. 684-694). SCITEPRESS. https://doi.org/10.5220/0004986106840694
    Thornton, Dallas ; Poel, Mannes ; van Hillegersberg, Jos ; Mueller, Roland ; van Capelleveen, Guido C. / Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data. Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014). SCITEPRESS, 2014. pp. 684-694
    @inproceedings{0ce5fddd6f3648e799fc34703c99d084,
    title = "Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data",
    abstract = "Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71{\%}) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.",
    keywords = "EWI-24984, Fraud detection, Medicaid, Healthcare Fraud, METIS-305987, IR-91956, Anomaly Detection, Data Mining, Outlier Detection",
    author = "Dallas Thornton and Mannes Poel and {van Hillegersberg}, Jos and Roland Mueller and {van Capelleveen}, {Guido C.}",
    note = "10.5220/0004986106840694",
    year = "2014",
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    doi = "10.5220/0004986106840694",
    language = "Undefined",
    isbn = "978-989-758-028-4",
    publisher = "SCITEPRESS",
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    booktitle = "Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014)",

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    Thornton, D, Poel, M, van Hillegersberg, J, Mueller, R & van Capelleveen, GC 2014, Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data. in Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014). SCITEPRESS, pp. 684-694, 16th International Conference on Enterprise Information Systems, ICEIS 2014, Lisbon, Portugal, 27/04/14. https://doi.org/10.5220/0004986106840694

    Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data. / Thornton, Dallas; Poel, Mannes; van Hillegersberg, Jos; Mueller, Roland; van Capelleveen, Guido C.

    Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014). SCITEPRESS, 2014. p. 684-694.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    AU - Thornton, Dallas

    AU - Poel, Mannes

    AU - van Hillegersberg, Jos

    AU - Mueller, Roland

    AU - van Capelleveen, Guido C.

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    AB - Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.

    KW - EWI-24984

    KW - Fraud detection

    KW - Medicaid

    KW - Healthcare Fraud

    KW - METIS-305987

    KW - IR-91956

    KW - Anomaly Detection

    KW - Data Mining

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    Thornton D, Poel M, van Hillegersberg J, Mueller R, van Capelleveen GC. Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data. In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS 2014). SCITEPRESS. 2014. p. 684-694 https://doi.org/10.5220/0004986106840694