Quality Improvement Spillovers: Evidence from the Hospital Readmissions Reduction Program
Bibliographic record
Abstract
Quality knowledge spillovers can enhance the overall effectiveness of quality improvement initiatives. We study the presence and moderators of such spillovers in a multitask service setting, specifically in hospital inpatient care. Leveraging a national quality improvement regulation, the Hospital Readmissions Reduction Program (HRRP), which offers partial incentives for hospitals to reduce readmissions, we employ difference-in-differences econometric models on a nationwide database and find positive quality spillovers in the healthcare sector. Our findings indicate that the implementation of HRRP led to a significant decrease in 30-day readmissions among patients with clinical conditions or insurance types that were not targeted by the policy. Additionally, we find that task similarity played a positive role in promoting quality spillovers, while a hospital’s operational focus on target patients (i.e., the proportion of hospital volume targeted by the policy) did not moderate these spillovers. Notably, we observe that hospitals achieved these quality improvements without increasing the intensity of care provided, and that meaningful improvements in quality were associated with up to a 3% reduction in hospitalization costs. This paper contributes novel insights into how regulators and policymakers can design narrow public policies and regulations that achieve broader results by exploiting the beneficial quality improvement spillovers of partial incentives. This paper was accepted by Ranjani Krishnan, accounting. Funding: Support for this research was provided by the Alberta School of Business Faculty Seed Grant and the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education, with funding from the Wisconsin Alumni Research Foundation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01062 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".