Longitudinal profiles of health care providers
Bibliographic record
Abstract
Provider profiling is the activity of collecting, comparing and reporting quality of care measures for individuals, groups, agencies and institutions that provide health care services. Univariate provider profiles, such as hospital-specific mortality rates, have been constructed using cross-sectional data based on posterior summaries or maximum likelihood estimates. As data continue to be collected over time, the construction and interpretation of longitudinal profiles of health care providers will become increasingly important. Longitudinal series can be used to improve the precision of estimates - a feature that is particularly important for providers who treat a small number of patients per year. We extend and apply hierarchical models to examine and classify provider performance over time using two examples, one in the area of cardiology and the other in mental health. Performance is evaluated using the squared Mahalanobis distance and posterior probabilities based on this distance. By comparing providers based on level and temporal trend simultaneously, conservative but comprehensive assessments of performance are possible. Furthermore, the longitudinal profiles developed are easily interpreted and flexible, making them of practical use to policy-makers.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".