Developing the Capacity for Rapid-Cycle Improvement at a Large Freestanding Children’s Hospital
Bibliographic record
Abstract
BACKGROUND: To develop the capacity for rapid-cycle improvement at the unit level, a large freestanding children's hospital designated 2 inpatient units with normal patient loads and workforce as pilot "Innovation Units" where frontline staff was trained to lead rigorous improvement portfolios. METHODS: Frontline staff received improvement training, and interdisciplinary teams brainstormed ideas for tests of change. Ideas were prioritized using an impact-effort evaluation and an assessment of how they aligned with high-level goals. A template for each test summarized the following: the opportunity for improvement, the test being conducted, dates for the tests, driver diagrams, metrics to measure effects, baseline data, results, findings, and next steps. Successful interventions were implemented and disseminated to other units. RESULTS: Multidisciplinary staff generated 150 improvement ideas and Innovation Units collectively ran >40 plan-do-study-act cycles. Of the 10 distinct improvement projects, elements of all 10 were deemed "successful" and fully implemented on the unit, and elements from 8 were spread to other units. More than 3 years later, elements of all of the successful improvements are still in practice in some form on the units, and each unit has tested >20 additional improvement ideas, using multiple plan-do-study-act cycles to refine them. CONCLUSIONS: The Innovation Unit model successfully engaged frontline staff in improvement work and established a sustainable system and framework for managing rigorous improvement portfolios at the unit level. Other hospitals and health care delivery settings may find our quality improvement approach helpful, especially because it is rooted in the microsystem of care delivery.
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 | 0.073 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".