Evidence of Upcoding in Pay-for-Performance Programs

Hamsa Bastani (), Joel Goh () and Mohsen Bayati ()
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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

Management Science, 2019, vol. 65, issue 3, 1042-1060

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.

Keywords: Medicare; pay-for-performance; upcoding; asymmetric information; quality control; detection (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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