AB009. How should doctors be paid?—a systematic review of the impacts of provider payment methods for primary care physicians on patient healthcare utilisation
Bibliographic record
Abstract
Background: Physician payment methods are valuable supply-side measures that may be reformed to achieve health policy objectives. However, few studies provide a comparison of the various methods of payment, to inform policy development. This review examines how payment methods for primary care physicians (PCPs) affect their patients’ healthcare utilisation, as a reflection of quality of care. Methods: PubMed, Embase, MEDLINE, EconLit, CINAHL Complete, and Web of Science were searched to identify papers in English investigating comparisons of payment methods for PCPs and their patients’ care usage. Payment methods included fee-for-service (FFS), capitation, salary, pay-for-performance (P4P), or a blend of these. Relevant outcomes were patient use of inpatient, outpatient or emergency care services. Results: Thirty-one studies involving 49,008 PCPs and 11,998,174 patients were included. The most commonly examined reimbursement mechanism was FFS (N=23), followed by capitation (N=18), P4P (N=13), and salary (N=6). Most outcomes concerned inpatient care (N=21), compared to emergency (N=15) and outpatient (N=1) care; some studies compared multiple methods and outcomes. Of the eight countries covered, the two most widely represented were USA (N=14) and Canada (N=9). The most consistent finding was improvement in outcomes under PCPs with a P4P adjunct compared to PCPs without; this was demonstrated in six of the nine studies. Of the thirteen studies comparing FFS and capitation reimbursement, four of seven studies with statistically significant outcomes showed that patients under FFS PCPs had lower care utilisation. No significant relationships were observed for studies comparing FFS and salary payments or investigating mixed payment models. Conclusions: This is the largest and most up-to-date study evaluating commonly used payment methods in terms of patient healthcare utilisation, and may serve as preliminary evidence in guiding policy reforms. Further research should employ more rigorously controlled designs, longer follow-up periods, and a wider range of quality outcomes to establish stronger conclusions.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".