IDEAS home Printed from https://ideas.repec.org/a/spr/hecrev/v14y2024i1d10.1186_s13561-023-00465-4.html
   My bibliography  Save this article

Upcoding in medicare: where does it matter most?

Author

Listed:
  • Keith A. Joiner

    (University of Arizona)

  • Jianjing Lin

    (Rensselaer Polytechnic Institute)

  • Juan Pantano

    (University of Arizona)

Abstract

Upcoding in Medicare has been a topic of interest to economists and policy makers for nearly 40 years. While upcoding is generally understood as “billing for services at higher level of complexity than the service actually pro- vided or documented,” it has a wide range of definitions within the literature. This is largely because the financial incentives across programs and aspects under the coding control of billing specialists and providers are different, and have evolved substantially over time, as has the published literature. Arguably, the primary importance of analyzing upcoding in different parts of Medicare is to inform policy makers on the magnitude of the process and to suggest approaches to mitigate the level of upcoding. Financial estimates for upcoding in traditional Medicare (Medicare Parts A and B), are highly variable, in part reflecting differences in methodology for each of the services covered. To resolve this variability, we used summaries of audit data from the Comprehensive Error Rate Testing program for the period 2010–2019. This program uses the same methodology across all forms of service in Medicare Parts A and B, allowing direct comparisons of upcoding magnitude. On average, upcoding for hospitalization under Part A represents $656 million annually (or 0.53% of total Part A annual expenditures) during our sample period, while up- coding for physician services under Part B is $2.38 billion annually (or 2.43% of Part B annual expenditures). These numbers compare to the recent consistent estimates from multiple different entities putting upcoding in Medicare Part C at $10–15 billion annually (or approximately 2.8–4.2% of Part C annual expenditures). Upcoding for hospitalization under Medicare Part A is small, relative to overall upcoding expenditures.

Suggested Citation

  • Keith A. Joiner & Jianjing Lin & Juan Pantano, 2024. "Upcoding in medicare: where does it matter most?," Health Economics Review, Springer, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:spr:hecrev:v:14:y:2024:i:1:d:10.1186_s13561-023-00465-4
    DOI: 10.1186/s13561-023-00465-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s13561-023-00465-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1186/s13561-023-00465-4?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. 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.
    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. Bowblis John R. & Brunt Christopher S. & Grabowski David C., 2016. "Competitive Spillovers and Regulatory Exploitation by Skilled Nursing Facilities," Forum for Health Economics & Policy, De Gruyter, vol. 19(1), pages 45-70, June.
    2. Christopher S. Brunt, 2015. "Medicare Part B Intensity and Volume Offset," Health Economics, John Wiley & Sons, Ltd., vol. 24(8), pages 1009-1026, August.
    3. 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.
    4. 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).
    5. Li‐Lin Liang, 2015. "Do Diagnosis‐Related Group‐Based Payments Incentivise Hospitals to Adjust Output Mix?," Health Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 454-469, April.
    6. Hessam Bavafa & Sergei Savin & Christian Terwiesch, 2021. "Customizing Primary Care Delivery Using E‐Visits," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4306-4327, November.
    7. Jamie O’Halloran & Anne Sophie Oxholm & Line Bjørnskov Pedersen & Dorte Gyrd‐Hansen, 2024. "Going the extra mile? General practitioners' upcoding of fees for home visits," Health Economics, John Wiley & Sons, Ltd., vol. 33(2), pages 197-203, February.
    8. Jillian Chown, 2020. "Financial Incentives and Professionals’ Work Tasks: The Moderating Effects of Jurisdictional Dominance and Prominence," Organization Science, INFORMS, vol. 31(4), pages 887-908, July.
    9. 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.
    10. Jack Hadley & James Reschovsky & James O’Malley & Bruce Landon, 2014. "Factors associated with geographic variation in cost per episode of care for three medical conditions," Health Economics Review, Springer, vol. 4(1), pages 1-19, December.
    11. Eric I Benchimol & Liam Smeeth & Astrid Guttmann & Katie Harron & David Moher & Irene Petersen & Henrik T Sørensen & Erik von Elm & Sinéad M Langan & RECORD Working Committee, 2015. "The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement," PLOS Medicine, Public Library of Science, vol. 12(10), pages 1-22, October.
    12. 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.
    13. 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.

    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:spr:hecrev:v:14:y:2024:i:1:d:10.1186_s13561-023-00465-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/13561 .

    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.