Performance Measurement in Surgery Through the National Quality Forum
Bibliographic record
Abstract
BACKGROUND: Performance measurement has become central to surgical practice. We systematically reviewed all endorsed performance measures from the National Quality Forum, the national clearing house for performance measures in health care, to identify measures relevant to surgical practice and describe measure stewardship, measure types, and identify gaps in measurement. STUDY DESIGN: Performance measures current to June 2014 were categorized by denominator statement as either assessing surgical practice in specific or as part of a mixed medical and surgical population. Measures were further classified by surgical specialty, Donabedian measure type, patients, disease and events targeted, reporting eligibility, and measure stewards. RESULTS: Of 637 measures, 123 measures assessed surgical performance in specific and 123 assessed surgical performance in aggregate. Physician societies (51 of 123, 41.5%) were more common than government agencies (32 of 123, 26.0%) among measure stewards for surgical measures, in particular, the Society for Thoracic Surgery (n = 32). Outcomes measures rather than process measures were common among surgical measures (62 of 123, 50.4%) compared with aggregate medical/surgical measures (46 of 123, 37.4%). Among outcomes measures, death alone was the most commonly specified outcome (24 of 62, 38.7%). Only 1 surgical measure addressed patient-centered care and only 1 measure addressed hospital readmission. We found 7 current surgical measures eligible for value-based purchasing. CONCLUSIONS: Surgical society stewards and outcomes measure types, particularly for cardiac surgery, were well represented in the National Quality Forum. Measures addressing patient-centered outcomes and the value of surgical decision-making were not well represented and may be suitable targets for measure innovation.
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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 | 0.213 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier 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".