Does financial incentive for diabetes management in the primary care setting reduce avoidable hospitalizations and mortality in high-income countries? A systematic review
Bibliographic record
Abstract
Effective diabetes management can prevent avoidable diabetes-related hospitalizations. This review examines the impact of financial incentives for diabetes management in primary care settings on diabetes-related hospitalizations, hospitalization costs, and premature mortality. To assess the evidence, we conducted a literature search of studies using five databases: Medline, Embase, Scopus, CINAHL and Web of Science. We examined the results by health insurance system, study quality or diabetes population (newly diagnosed diabetes). We identified 32 articles ranging from fair- to high-quality: 19 articles assessed the relationship between financial incentives for diabetes management and hospitalizations, 8 assessed hospitalization costs, and 15 assessed mortality. Many studies found that financial incentives for diabetes management reduced hospitalizations, while a few found no effects. Similar findings were evident for hospitalization costs and mortality. The results did not differ by the type of health insurance system, but the quality of the studies did matter; most high-quality studies reported reduced hospitalizations and/or mortality. We also found that financial incentives tend to be beneficial for patients with newly diagnosed diabetes. We conclude that well-designed diabetes management incentives can reduce diabetes-related hospitalizations, especially for newly diagnosed diabetes patients.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".