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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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    1. Zeltzer, Dan, 2017. "Gender Homophily in Referral Networks: Consequences for the Medicare Physician Earnings Gap," IZA Discussion Papers 11230, Institute of Labor Economics (IZA).
    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, 2015. "Upcoding: Evidence from Medicare on Squishy Risk Adjustment," NBER Working Papers 21222, National Bureau of Economic Research, Inc.
    4. 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.
    5. Congressional Budget Office, 2014. "The 2014 Long-Term Budget Outlook," Reports 45471, Congressional Budget Office.
    6. Congressional Budget Office, 2014. "The 2014 Long-Term Budget Outlook," Reports 45471, Congressional Budget Office.
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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. 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.
    3. Hafner, Lucas & Reif, Simon & Seebauer, Michael, 2017. "Physician behavior under prospective payment schemes: Evidence from artefactual field and lab experiments," FAU Discussion Papers in Economics 18/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

    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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