Optimizing Physician Payment Models to Address Health System Priorities: Perspectives from Specialist Physicians
Bibliographic record
Abstract
OBJECTIVE: Despite well-documented data on the mixed impact of physician payment models, there is limited evidence on how to enhance existing payment model designs. This study examines the approaches to optimizing payment models from the perspective of specialist physicians to better support patient and physician experience and other health system objectives. METHOD: Semi-structured interviews were conducted with 32 specialist physicians across Alberta, Canada. Data from the interviews were analyzed using a framework approach. RESULTS: Respondents emphasized the need to incentivize physicians with the right blend of financial and non-financial incentives, including physician wellness. Respondents also highlighted the need for physician involvement and accountability to optimize the value of physician payment models. CONCLUSION: To optimize physician payment models, it may be useful to include a blend of financial and non-financial incentives with clear accountability measures as this may better align physician practice with health system priorities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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".