Can Composite Measures Provide a Different Perspective on Provider Performance Than Individual Measures?
Bibliographic record
Abstract
BACKGROUND: Composite measures, which aggregate performance on individual measures into a summary score, are increasingly being used to evaluate facility performance. There is little understanding of the unique perspective that composite measures provide. OBJECTIVE: To examine whether high/low (ie, high or low) performers on a composite measures are also high/low performers on most of the individual measures that comprise the composite. METHODS: We used data from 2 previous studies, one involving 5 measures from 632 hospitals and one involving 28 measures from 112 Veterans Health Administration (VA) nursing homes; and new data on hospital readmissions for 3 conditions from 131 VA hospitals. To compare high/low performers on a composite to high/low performers on the component measures, we used 2-dimensional tables to categorize facilities into high/low performance on the composite and on the individual component measures. RESULTS: In the first study, over a third of the 162 hospitals in the top quintile based on the composite were in the top quintile on at most 1 of the 5 individual measures. In the second study, over 40% of the 27 high-performing nursing homes on the composite were high performers on 8 or fewer of the 28 individual measures. In the third study, 20% of the 61 low performers on the composite were low performers on only 1 of the 3 individual measures. CONCLUSIONS: Composite measures can identify as high/low performers facilities that perform "pretty well" (or "pretty poorly") across many individual measures but may not be high/low performers on most of them.
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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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".