Creating Value Through Learning Health Systems
Bibliographic record
Abstract
Design, implementation, and evaluation of effective multicomponent interventions typically take decades before value is realized even when value can be measured. Value-based health care, an approach to improving patient and health system outcomes, is a way of organizing health systems to transform outcomes and achieve the highest quality of care and the best possible outcomes with the lowest cost. We describe 2 case studies of value-based health care optimized through a learning health system framework that includes Strategic Clinical Networks. Both cases demonstrate the acceleration of evidence to practice through scientific, financial, structural administrative supports and partnerships. Clinical practice interventions in both cases, one in perioperative services and the other in neonatal intensive care, were implemented across multiple hospital sites. The practical application of using an innovation pipeline as a structural process is described and applied to these cases. A value for money improvement calculator using a benefits realization approach is presented as a mechanism/tool for attributing value to improvement initiatives that takes advantage of available system data, customizing and making the data usable for frontline managers and decision makers. Health care leaders will find value in the descriptions and practical information provided.
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 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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".