The Relationship Between Financial Incentives and Quality of Diabetes Care in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: We assessed the impact of a diabetes incentive code introduced for primary care physicians in Ontario, Canada, in 2002 on quality of diabetes care at the population and patient level. RESEARCH DESIGN AND METHODS: We analyzed administrative data for 757,928 Ontarians with diabetes to examine the use of the code and receipt of three evidence-based monitoring tests from 2006 to 2008. We assessed testing rates over time and before and after billing of the incentive code. RESULTS: One-quarter of Ontarians with diabetes had an incentive code billed by their physician. The proportion receiving the optimal number of all three monitoring tests (HbA(1c), cholesterol, and eye tests) rose gradually from 16% in 2000 to 27% in 2008. Individuals who were younger, lived in rural areas, were not enrolled in a primary care model, or had a mental illness were less likely to receive all three recommended tests. Patients with higher numbers of incentive code billings in 2006-2008 were more likely to receive recommended testing but also were more likely to have received the highest level of recommended testing prior to introduction of the incentive code. Following the same patients over time, improvement in recommended testing was no greater after billing of the first incentive code than before. CONCLUSIONS: The diabetes incentive code led to minimal improvement in quality of diabetes care at the population and patient level. Our findings suggest that physicians who provide the highest quality care prior to incentives may be those most likely to claim incentive payments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".