IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0261363.html
   My bibliography  Save this article

Luck of the draw: Role of chance in the assignment of medicare readmissions penalties

Author

Listed:
  • Andrew D Wilcock
  • Sushant Joshi
  • José Escarce
  • Peter J Huckfeldt
  • Teryl Nuckols
  • Ioana Popescu
  • Neeraj Sood

Abstract

Pay-for-performance programs are one strategy used by health plans to improve the efficiency and quality of care delivered to beneficiaries. Under such programs, providers are often compared against their peers in order to win bonuses or face penalties in payment. Yet luck has the potential to affect performance assessment through randomness in the sorting of patients among providers or through random events during the evaluation period. To investigate the impact luck can have on the assessment of performance, we investigated its role in assigning penalties under Medicare’s Hospital Readmissions Reduction Policy (HRRP), a program that penalizes hospitals with excess readmissions. We performed simulations that estimated program hospitals’ 2015 readmission penalties in 1,000 different hypothetical fiscal years. These hypothetical fiscal years were created by: (a) randomly varying which patients were admitted to each hospital and (b) randomly varying the readmission status of discharged patients. We found significant differences in penalty sizes and probability of penalty across hypothetical fiscal years, signifying the importance of luck in readmission performance under the HRRP. Nearly all of the impact from luck arose from events occurring after hospital discharge. Luck played a smaller role in determining penalties for hospitals with more beds, teaching hospitals, and safety-net hospitals.

Suggested Citation

  • Andrew D Wilcock & Sushant Joshi & José Escarce & Peter J Huckfeldt & Teryl Nuckols & Ioana Popescu & Neeraj Sood, 2021. "Luck of the draw: Role of chance in the assignment of medicare readmissions penalties," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-14, December.
  • Handle: RePEc:plo:pone00:0261363
    DOI: 10.1371/journal.pone.0261363
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0261363
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0261363&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0261363?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. Tim C. Hesterberg, 2015. "What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 371-386, November.
    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. Mozhgan Alirezaei Dizicheh & Nasrollah Iranpanah & Ehsan Zamanzade, 2021. "Bootstrap Methods for Judgment Post Stratification," Statistical Papers, Springer, vol. 62(5), pages 2453-2471, October.
    2. Casalin, Fabrizio & Dia, Enzo, 2019. "Information and reputation mechanisms in auctions of remanufactured goods," International Journal of Industrial Organization, Elsevier, vol. 63(C), pages 185-212.
    3. Hakim, Adam & Klorfeld, Shira & Sela, Tal & Friedman, Doron & Shabat-Simon, Maytal & Levy, Dino J., 2021. "Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 770-791.
    4. Jale Samuwai & Jeremy Maxwell Hills, 2018. "Assessing Climate Finance Readiness in the Asia-Pacific Region," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    5. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    6. Timothy G. Gregoire & David L. R. Affleck, 2018. "Estimating Desired Sample Size for Simple Random Sampling of a Skewed Population," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 184-190, April.
    7. Yulong Xie & Mark Halverson & Rosemarie Bartlett & Yan Chen & Michael Rosenberg & Todd Taylor & Jeremiah Williams & Michael Reiner, 2020. "Evaluating Building Energy Code Compliance and Savings Potential through Large-Scale Simulation with Models Inferred by Field Data," Energies, MDPI, vol. 13(9), pages 1-19, May.
    8. Samanwoy Mukhopadhyay & Pravat K Thatoi & Abhay D Pandey & Bidyut K Das & Balachandran Ravindran & Samsiddhi Bhattacharjee & Saroj K Mohapatra, 2017. "Transcriptomic meta-analysis reveals up-regulation of gene expression functional in osteoclast differentiation in human septic shock," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-17, February.
    9. Subburaj Alagarsamy & Sangeeta Mehrolia & Sonia Mathew, 2021. "How Green Consumption Value Affects Green Consumer Behaviour: The Mediating Role of Consumer Attitudes Towards Sustainable Food Logistics Practices," Vision, , vol. 25(1), pages 65-76, March.
    10. Au Yong Lyn, Audrey, 2022. "Vocational training and employment outcomes of domestic violence survivors: Evidence from Chihuahua City," International Journal of Educational Development, Elsevier, vol. 89(C).
    11. Fırat Bilgel & Burhan Can Karahasan, 2024. "Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 793-833, February.
    12. Benjamin D. Evans & Piotr Słowiński & Andrew T. Hattersley & Samuel E. Jones & Seth Sharp & Robert A. Kimmitt & Michael N. Weedon & Richard A. Oram & Krasimira Tsaneva-Atanasova & Nicholas J. Thomas, 2021. "Estimating disease prevalence in large datasets using genetic risk scores," Nature Communications, Nature, vol. 12(1), pages 1-12, December.

    More about this item

    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:plo:pone00:0261363. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.