JOURNAL ARTICLE

Quality Improvement Spillovers: Evidence from the Hospital Readmissions Reduction Program.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 7. P. 6112 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Soltani, Mohamad; Batt, Robert J.; Bavafa, Hessam 3 of 3

Abstract

This article examines quality improvement spillovers resulting from partial incentives in a multitask service setting, focusing on the Hospital Readmissions Reduction Program (HRRP), a U.S. national policy targeting reductions in 30-day hospital readmissions for Medicare patients aged 65 and older with specific clinical conditions. Using difference-in-differences models on a nationwide database, the study finds that HRRP not only reduced readmissions for targeted patients but also generated positive spillovers to nontarget patients—those with either nontarget conditions or private insurance—especially when clinical conditions were similar to the targeted ones. The analysis shows that these spillovers occurred without increasing care intensity or hospitalization costs, and that hospital operational focus on target patients did not moderate the spillover effects. These findings suggest that narrowly targeted quality improvement policies can achieve broader impacts through knowledge spillovers, offering insights for policymakers designing partial incentive programs in healthcare and other multitask organizational settings.

Additional Information

  • Source:Management Science (INFORMS). 2025/07, Vol. 71, Issue 7, p6112
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.01062
  • Accession Number:187524670
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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