Effect of Regional Cooperation on Efficiency of Medical Care Delivery in Secondary Medical Areas of Japan
Bibliographic record
Abstract
The Japanese population is aging and requires regional health facilities to cooperate to use medical resources efficiently. This study evaluated the impact of regional cooperation on the efficiency of medical care delivery in secondary medical areas. The discharge adjustment implementation rate of each secondary medical area was used as a proxy for regional cooperation. The study data were obtained from publicly available sources. The efficiency scores of secondary medical areas were calculated using the input-oriented Banker–Charnes–Cooper model for Data Envelopment Analysis. The inputs used were the number of general beds and the average length of hospital stay for each secondary medical area. The outputs used were the number of discharged patients and inpatient medical expenses per person. In addition, the relationship between discharge adjustment implementation rates and efficiency scores were assessed using tobit multiple regression analysis. The models were adjusted for the 7 variables. Ten secondary medical areas had an efficiency score of 1.00 (i.e., highest efficiency). Tobit regression analysis was performed on the 340 secondary medical areas for which efficiency scores were obtained. The discharge adjustment implementation rates and efficiency scores were significantly positively correlated (p = 0.032). While studies that quantitatively evaluate regional cooperation and efficiency are limited, these findings suggest that implementing regional cooperation may improve the efficiency of medical care delivery in secondary medical areas.
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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 | 0.033 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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; both teacher heads agree on what is shown here.
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".