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Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked

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  • Hanming Fang
  • Qing Gong

Abstract

Medicare overbilling refers to the phenomenon that providers report more and/or higher-intensity service codes than actually delivered to receive higher Medicare reimbursement. We propose a novel and easy-to-implement approach to detect potential overbilling based on the hours worked implied by the service codes physicians submit to Medicare. Using the Medicare Part B Fee-for-Service (FFS) Physician Utilization and Payment Data in 2012 and 2013 released by the Centers for Medicare and Medicaid Services (CMS), we first construct estimates for physicians' hours spent on Medicare Part B FFS beneficiaries. Despite our deliberately conservative estimation procedure, we find that about 2,300 physicians, or 3% of those with a significant fraction of Medicare Part B FFS services, have billed Medicare over 100 hours per week. We consider this implausibly long hours. As a benchmark, the maximum hours spent on Medicare patients by physicians in National Ambulatory Medical Care Survey data are 50 hours in a week. Interestingly, we also find suggestive evidence that the coding patterns of the flagged physicians seem to be responsive to financial incentives: within code clusters with different levels of service intensity, they tend to submit more higher intensity service codes than unflagged physicians; moreover, they are more likely to do so if the marginal revenue gain from submitting mid- or high-intensity codes is relatively high.

Suggested Citation

  • Hanming Fang & Qing Gong, 2016. "Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked," NBER Working Papers 22084, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22084
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    References listed on IDEAS

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    Cited by:

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.

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    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

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