Comparison Actual Conversion Factor with Estimated Conversion Factor by Fee Adjustment Model Reflecting Health Service Volume
Bibliographic record
Abstract
Background: Price control alone may not successfully restrain growth in health expenditures. This study aimed to propose fee adjustment model suitable for Korea reflecting health service volume and to clarify applicability of the model by comparing actual conversion factor with estimated conversion factor from simulation of this model. Methods: Fee adjustment model was developed based on Alberta's fee adjustment formula in Canada and 7 alternatives were assessed according to diversely applied parameters of the model. Results: Estimated conversion factors of the tertiary care hospital and the hospital were lower than actual conversion factors, since the utilization of heath service has been increased. However, there was no big difference between estimated conversion factors and actual conversion factors of the general hospital and the clinic. Eventually this fee adjustment model could estimate proper conversion factor reflecting health service volume. Conclusion: This model may be applicable to the mechanism as determining conversion factor between insurer and provider via negotiation and controling growth in health expenditures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 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".