Evidence of Upcoding in Pay-for-Performance Programs

• 成果类型: Article
• 署名作者: Bastani, Hamsa; Goh, Joel; Bayati, Mohsen
• 署名单位: University of Pennsylvania; International Business Machines (IBM); IBM USA; National University of Singapore; Harvard University; Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2017.2996
• 发表日期: 2019
• 页码: 1042-1060
• 关键词: Medicare; Pay-for-performance; upcoding; asymmetric information; quality control; detection

摘要

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.