Impact of oncologist payment method on health care outcomes, costs, quality: a rapid review
Bibliographic record
Abstract
BACKGROUND: The incidence of cancer and the cost of its treatment continue to rise. The effect of these dual forces is a major burden on the system of health care financing. One cost containment approach involves changing the way physicians are paid. Payers are testing reimbursement methods such as capitation and prospective payment while also evaluating how the changes impact health outcomes, resource utilization, and quality of care. The purpose of this study is to identify evidence related to physician payment methods' impacts, with a focus on cancer control. METHODS: We conducted a rapid review. This involved defining eligibility criteria, identifying a search strategy, performing study selection according to the eligibility criteria, and abstracting data from included studies. This process was accompanied by a gray literature search for special topics. RESULTS: The incentives in fee-for-service payment systems generally lead to health care services being applied inconsistently because providers practice independently with few systems in place for developing treatment protocols and practice reviews. This inconsistency is pronounced in cancer care because much of the total per patient spending occurs in the last month of life. Some insurers are predicting that this variation can be reduced through the use of prospective or bundled payments combined with decision support systems. Workload, recruitment, and retention are all affected by changes to physician payment models; effects seem to be magnified in the specialist context as their several extra years of training lower their overall supply. CONCLUSIONS: Experimentation with physician payment methods has tended to neglect cancer care providers. Policymakers designing cancer-focused physician reimbursement pilot programs should incorporate quality measurement since very ill patients may receive too little treatment when payment models do not cover oncologists' total costs, e.g., fee-for-service systems whose prices do not account for the possible presence of other diseases.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".