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Evidence of Upcoding in Pay-for-Performance Programs

Author

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  • Hamsa Bastani

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104; IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Joel Goh

    (NUS Business School, National University of Singapore, Singapore 119245; Harvard Business School, Boston, Massachusetts 02163)

  • Mohsen Bayati

    (Stanford Graduate School of Business, Stanford, California 94305)

Abstract

Recent Medicare legislation seeks to improve patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates and instead relies on providers accurately self-reporting HAIs in claims to correctly assess penalties. Consequently, the incentives for providers to improve service quality may disappear if providers upcode , i.e., misreport HAIs (possibly unintentionally) in a manner that increases reimbursement or avoids financial penalties. Identifying upcoding in claims data is challenging because of unobservable confounders (e.g., patient risk). We leverage state-level variations in adverse event reporting regulations and instrumental variables to discover contradictions in HAI and present-on-admission (POA) infection reporting rates that are strongly suggestive of upcoding. We conservatively estimate that 10,000 out of 60,000 annual reimbursed claims for POA infections (18.5%) were upcoded HAIs, costing Medicare $200 million. Our findings suggest that self-reported quality metrics are unreliable and, thus, that recent legislation may result in unintended consequences.

Suggested Citation

  • Hamsa Bastani & Joel Goh & Mohsen Bayati, 2019. "Evidence of Upcoding in Pay-for-Performance Programs," Management Science, INFORMS, vol. 65(3), pages 1042-1060, March.
  • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:3:p:1042-1060
    DOI: 10.1287/mnsc.2017.2996
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    References listed on IDEAS

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    Cited by:

    1. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    2. Gómez, Maria Fernanda & González-Velosa, Carolina, 2023. "Can a Pay-for- Performance Program Help the Vulnerable find Jobs during a Pandemic?: Experimental Evidence from Empleate in Colombia," IDB Publications (Working Papers) 12982, Inter-American Development Bank.
    3. Cadena, Brian C. & Smith, Austin C., 2022. "Performance pay, productivity, and strategic opt-out: Evidence from a community health center," Journal of Public Economics, Elsevier, vol. 206(C).
    4. 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.
    5. Fainman, Emily Zhu & Kucukyazici, Beste, 2020. "Design of financial incentives and payment schemes in healthcare systems: A review," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    6. Kartik K. Ganju & Hilal Atasoy & Paul A. Pavlou, 2022. "Do Electronic Health Record Systems Increase Medicare Reimbursements? The Moderating Effect of the Recovery Audit Program," Management Science, INFORMS, vol. 68(4), pages 2889-2913, April.
    7. Guihua Wang & Jun Li & Wallace J. Hopp, 2022. "An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies," Management Science, INFORMS, vol. 68(5), pages 3399-3418, May.
    8. Diwas Singh KC & Stefan Scholtes & Christian Terwiesch, 2020. "Empirical Research in Healthcare Operations: Past Research, Present Understanding, and Future Opportunities," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 73-83, January.

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