Value-based Care and Quality Improvement in Perioperative Neuroscience
Bibliographic record
Abstract
Value-based care and quality improvement are related concepts used to measure and improve clinical care. Value-based care represents the relationship between the incremental gain in outcome for patients and cost efficiency. It is achieved by identifying outcomes that are important to patients, codesigning solutions using multidisciplinary teams, measuring both outcomes and costs to drive further improvements, and developing partnerships across the health system. Quality improvement is focused on process improvement and compliance with best practice, and often uses "Plan-Do-Study-Act" cycles to identify, test, and implement change. Validated, standardized core outcome sets for perioperative neuroscience are currently lacking, but neuroanesthesiologists can consider using traditional clinical indicators, patient-reported outcomes measures, and perioperative core outcome measures. Several examples of bundled care solutions have been successfully implemented in perioperative neuroscience to increase value; for example, enhanced recovery for spine surgery, delirium reduction pathways, and same-day discharge craniotomy. This review proposes potential individual- and system-based solutions to address barriers to value-based care and quality improvement in perioperative neuroscience.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".