Physician Response to Pay-for-Performance: Evidence from a Natural Experiment
Bibliographic record
Abstract
Explicit financial incentives, especially pay-for-performance (P4P) incentives, have been extensively employed in recent years by health plans and governments in an attempt to improve the quality of health care services.This study exploits a natural experiment in the province of Ontario, Canada to identify empirically the impact of pay-for-performance (P4P) incentives on the provision of targeted primary care services, and whether physicians' responses differ by age, practice size and baseline compliance level.We use an administrative data source which covers the full population of the province of Ontario and nearly all the services provided by practicing primary care physicians in Ontario.With an individual-level data set of physicians, we employ a difference-in-differences approach that controls for both "selection on observables" and "selection on unobservables" that may cause estimation bias in the identification.We also implemented a set of robustness checks to control for confounding from the other contemporary interventions of the primary care reform in Ontario.The results indicate that, while all responses are of modest size, physicians responded to some of the financial incentives but not the others.The differential responses appear related to the cost of responding and the strength of the evidence linking a service with quality.Overall, the results provide a cautionary message regarding the effectiveness of pay-for-performance schemes for increasing quality of care.
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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 | 0.057 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".