[Towards a more equitable distribution of resources and assessment of quality of care: validation of a comorbidity based case-mix system].
Bibliographic record
Abstract
BACKGROUND: Equitable distribution of healthcare resources and fair assessments of providers' performance necessitates adjusting for case-mix. The feasibility and validity of applying case-mix measures, based on inpatient and outpatient diagnoses, has yet to be tested in Israel. AIMS: Assessment of the feasibility and validity of applying the Johns-Hopkins University Adjusted Clinical Groups (JHU-ACG) case-mix system, using diagnoses from hospitalizations or physician visits, at Clalit Health Services (CHS). METHODS: A representative sample of 117,355 enrollees during 2006. The distribution of ACG morbidity groups and relative resource weights in CHS and the degree to which it corresponds to ACGs' distribution in other countries was examined. The degree to which ACGs can explain utilization of primary and specialty care in CHS was determined. RESULTS: ACGs explained a large percent of the variance in primary care and specialist visits (R2 = 38-54%), better than age and gender alone (R2 =12-13%). A high degree of correlation was found between the distribution of the population into ACG groups in CHS and samples from Canada or the United States (r = 0.91), and between the relative resource use for each ACG at CHS compared to the Canadian and US samples (r = 0.78-0.98). CONCLUSIONS: The JHU-ACG case-mix system can be applied in the Largest healthcare organization in Israel based on diagnoses generated at hospitalizations and physician visits. The system can now be applied for a variety of purposes, including resource allocation according to medical need, and for conducting fair assessments of providers' performance, which are currently being tested by CHS.
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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 | 0.041 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".