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Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked

Author

Listed:
  • Hanming Fang

    (Department of Economics, University of Pennsylvania)

  • Qing Gong

    (Department of Economics, UNC Chapel Hill)

Abstract
Medicare over billing 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 over billing 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," PIER Working Paper Archive 16-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 07 Mar 2016.
    • Handle: RePEc:pen:papers:16-006
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    References listed on IDEAS

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    1. Dan Zeltzer, 2020. "Gender Homophily in Referral Networks: Consequences for the Medicare Physician Earnings Gap," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 169-197, April.
    2. Christopher S. Brunt, 2011. "CPT fee differentials and visit upcoding under Medicare Part B," Health Economics, John Wiley & Sons, Ltd., vol. 20(7), pages 831-841, July.
    3. Michael Geruso & Timothy Layton, 2020. "Upcoding: Evidence from Medicare on Squishy Risk Adjustment," Journal of Political Economy, University of Chicago Press, vol. 128(3), pages 984-1026.
    4. Leemore Dafny & David Dranove, 2009. "Regulatory Exploitation and Management Changes: Upcoding in the Hospital Industry," Journal of Law and Economics, University of Chicago Press, vol. 52(2), pages 223-250, May.
    5. John R. Bowblis & Christopher S. Brunt, 2014. "Medicare Skilled Nursing Facility Reimbursement And Upcoding," Health Economics, John Wiley & Sons, Ltd., vol. 23(7), pages 821-840, July.
    6. Congressional Budget Office, 2014. "The 2014 Long-Term Budget Outlook," Reports 45471, Congressional Budget Office.
    7. Congressional Budget Office, 2014. "The 2014 Long-Term Budget Outlook," Reports 45471, Congressional Budget Office.
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    Citations

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    Cited by:

    1. 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.
    2. 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, MIT Press, vol. 5(4), pages 481-508, Fall.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

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    More about this item

    Keywords

    Medicare; Overbilling; Hours worked;
    All these keywords.

    JEL classification:

    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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