Investigating the Spillover Mechanisms of Payment Incentives on the Outcomes for Non‐Targeted Patients
Bibliographic record
Abstract
Payment reforms in healthcare can have spillover effects on the care experienced by non-targeted patients treated by the same provider. Few empirical studies have quantitatively investigated the mechanisms behind these effects. We formulate theory-driven hypotheses to investigate the spillover mechanisms of a regional payment reform in the English National Health Service, using linked patient-physician data and difference-in-differences methods. We show that regional payment changes were associated with an increase in mortality of 0.321 percentage points (S.E. 0.114) for non-targeted emergency patients who were treated by physicians with no exposure to the incentives, compared to control regions. In contrast, the mortality rate for non-targeted patients reduced by 0.008 percentage points (S.E. 0.002) for every additional targeted patient treated per quarter by their physician. These findings were consistent across a range of sensitivity analyses. The findings suggest that providers diverted resources away from non-targeted patients but that patients benefitted from physicians learning from the incentives. We demonstrate how the formulation of theory-driven hypotheses about spillover mechanisms can improve the understanding of how and where spillover effects may occur, contributing to research design and policymaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".