Learning Health Systems and Improvement Science in Neurology
Bibliographic record
Abstract
Although the quality movement in healthcare in the United States has been maturing for the last several decades, neurology remains a frontier of work related to learning healthcare systems (LHS) and the science of improvement. This review describes the use of LHS models in neurology and the use of Improvement Science to advance position system changes and improve care. LHSs are broadly understandable, widely supported, and have a developing yet proven track record. However, there are distinct challenges at multiple levels in successful implementation, as well as nuances related to variability in practice patterns and institutions. This review outlines these hurdles and approaches to addressing them. There are examples of effective work currently being conducted in this emerging field, with an emphasis on two subspecialties that have been the primary early adopters of these models and methodology within neurology: stroke and epilepsy. As LHS models take shape in neurology subspecialties, there will be an ongoing need for collaboration and iterative change to support continuous improvement in systems of care and improve outcomes for patients with neurologic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".