PP265 Application Of A Case-Mix Method For Medical Consumables Management In Anhui Province, China Using Healthcare Big Data
Bibliographic record
Abstract
Introduction The case-mix method involves combining cases with similar complexities and medical services. The process of treating one episode of the disease and receiving treatment is the research unit, thus achieving different medical units. The feasibility of the calculation method is verified by calculating the public hospital consumption ratio, medical income, health materials expenditure indicators, and the differences between the various types of surgical combinations. A decision-making basis can then be provided for the creation of government indicator standards. Methods Medical records and data on the expenditure of medical consumables for the first and fourth quarters of 2017 were collected from seven third-class provincial hospitals. The medical consumption ratio for different diseases and surgical methods was calculated for the case-mix groups using a weighting method. Data were analyzed by descriptive statistics and the independent samples t-test. Results There were significant differences in the proportions of combined use for different types of diseases. The same combination also had significant differences between different hospitals. In the fourth quarter of 2017, the operating group's consumption ratio was significantly lower than in the first quarter (p = 0.000). Conclusions It is reasonable to calculate the proportion of consumption by combined weighted analysis, which is also fairer for hospitals with better technical levels. This calculation method can be used by governments to manage the use and cost of medical consumables in hospitals.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".