‘Hospital Readmissions Reduction Program does not provide the right incentives: Issues and remedies’
by Tolga Tezcan
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The Hospital Readmissions Reduction Program (HRRP) reduces Medicare payments to hospitals with higher-than-expected readmission rates where the expected read mission rate for each hospital is determined based on the readmission levels at other hospitals. Although similar relative-performance-based schemes are shown to lead to socially optimal outcomes in other settings (e.g., cost cutting efforts),HRRP differs from these schemes in three respects: (i) deviation from the targets are adjusted using a multiplier; (ii) the total financial penalty for a hospital with higher-than-expected readmission rate is capped; and (iii) hospitals with lower-than-expected readmission rates do not receive bonus payments. We study three regulatory schemes derived from HRRP to determine the impact of each feature, and use a principal-agent model to show that: (i) HRRP over-penalizes hospitals with excess readmissions because of the multiplier and its effect can be substantial; (ii) having a penalty cap can curtail the effect of financial incentives and result in a no-equilibrium outcome when the cap is too low; and (iii) not allowing bonus payments leads to many alternative symmetric equilibria, including one where hospitals exert no effort to reduce readmissions. These results show that HRRP does not provide the right incentives for hospitals to reduce readmissions. Next we show that a bundled payment type reimbursement method, which reimburses hospitals once for each episode of care (including readmissions), leads to socially optimal cost and readmissions reduction efforts. Finally we show that, when delays to accessing care are inevitable, there imbursement schemes need to provide additional incentives for hospitals to invest sufficiently in capacity.
Joint work with Kenan Arifoglu School of Management, University College London and Hang REN, School of Business, George Mason University,
Short bio: Professor Tolga Tezcan’s research focuses on how to design and manage service systems under uncertainty with applications in customer service and healthcare systems. His recent research focuses innovative reimbursement methods in health care. He has worked with customer call centres, customer service chat systems, emergency departments, and hospitals. He was awarded the NSF Career Award in2010 for his research and serves on the editorial boards of the journals Management Science, Operations Research, and Stochastic Systems.