Quality Based Incentive Payments and the Achievement of Health Care Service Delivery Goals
Bibliographic record
Abstract
Quality Based Incentive Payments (QBIP) is a relatively new method of remuneration in the health care field, which rewards physicians based on performance. Examples include Performance based Reimbursement under the Quality and Outcomes Framework in the U.K., and Pay for Performance programs across the U.S. Most often, QBIPs are used in combination with traditional payment methods, which include fee for service, capitation, or salaries. Each type of traditional payment structure creates incentives that support some goals of health care service delivery, but undermine other goals. For example, the fee for service system supports productivity, but undermines acceptance of risky patients. This paper explores the extent to which QBIPs can be used to compliment traditional remuneration methods in inciting achievement of health care service delivery goals. A categorization of various modalities of QBIPs is provided and incentives created are discussed. Each traditional payment method is matched with the best fitting QBIP modality, so as to fill gaps within traditional payment methods with incentives created by QBIPs. The broader context within which QBIPs are implemented is also discussed.
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 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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".