IDEAS home Printed from https://ideas.repec.org/a/aea/aecrev/v110y2020i12p4004-10.html
   My bibliography  Save this article

Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply

Author

Listed:
  • Hanming Fang
  • Qing Gong

Abstract

Matsumoto (2020) pointed out data and coding errors in Fang and Gong (2017). We show that these errors have limited impacts: all qualitative findings remain after correcting them. Matsumoto also discussed potential service overcounting in the aggregated utilization data we used to illustrate our method, and then quantified the extent of overcounting with a sample of Medicare claims. We acknowledge the issue but discuss the noise and the bias in his quantification. Overall, our proposed method remains useful, as regulators who are interested in applying the method are unlikely to be subject to the data limitations.

Suggested Citation

  • Hanming Fang & Qing Gong, 2020. "Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Reply," American Economic Review, American Economic Association, vol. 110(12), pages 4004-4010, December.
    • Handle: RePEc:aea:aecrev:v:110:y:2020:i:12:p:4004-10
    DOI: 10.1257/aer.20191970
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/aer.20191970
    Download Restriction: no
    File URL: https://doi.org/10.3886/E119192V1
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/aer.20191970.appx
    Download Restriction: no
    File URL: https://www.aeaweb.org/doi/10.1257/aer.20191970.ds
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://libkey.io/10.1257/aer.20191970?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hanming Fang & Qing Gong, 2017. "Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked," American Economic Review, American Economic Association, vol. 107(2), pages 562-591, February.
    2. Brett Matsumoto, 2020. "Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment," American Economic Review, American Economic Association, vol. 110(12), pages 3991-4003, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ronelle Burger & Canh Thien Dang & Trudy Owens, 2017. "Better performing NGOs do report more accurately: Evidence from investigating Ugandan NGO financial accounts," Discussion Papers 2017-10, University of Nottingham, CREDIT.
    2. Cook, Amanda & Averett, Susan, 2020. "Do hospitals respond to changing incentive structures? Evidence from Medicare’s 2007 DRG restructuring," Journal of Health Economics, Elsevier, vol. 73(C).
    3. Dang, Canh Thien & Owens, Trudy, 2020. "Does transparency come at the cost of charitable services? Evidence from investigating British charities," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 314-343.
    4. Victoria Perez & Coady Wing, 2019. "Should We Do More to Police Medicaid Fraud? Evidence on the Intended and Unintended Consequences of Expanded Enforcement," American Journal of Health Economics, University of Chicago Press, vol. 5(4), pages 481-508, Fall.
    5. Xidong Guo, 2024. "An analysis of a rural hospital's investment decision under different payment systems," Health Economics, John Wiley & Sons, Ltd., vol. 33(4), pages 714-747, April.
    6. David C Chan & Michael J Dickstein, 2019. "Industry Input in Policy Making: Evidence from Medicare," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1299-1342.
    7. Thuy Nguyen & Victoria Perez, 2020. "Privatizing Plaintiffs: How Medicaid, the False Claims Act, and Decentralized Fraud Detection Affect Public Fraud Enforcement Efforts," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(4), pages 1063-1091, December.
    8. Simon Reif & Lucas Hafner & Michael Seebauer, 2020. "Physician Behavior under Prospective Payment Schemes—Evidence from Artefactual Field and Lab Experiments," IJERPH, MDPI, vol. 17(15), pages 1-37, July.
    9. 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.
    10. Shubhranshu Shekhar & Jetson Leder-Luis & Leman Akoglu, 2023. "Unsupervised Machine Learning for Explainable Health Care Fraud Detection," NBER Working Papers 30946, National Bureau of Economic Research, Inc.
    11. David C. Chan, Jr & Michael J. Dickstein, 2018. "Industry Input in Policymaking: Evidence from Medicare," NBER Working Papers 24354, National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aecrev:v:110:y:2020:i:12:p:4004-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.