Optimizing Quality, Service, and Cost Through Innovation
Bibliographic record
Abstract
With dramatic increases in health care costs and growing concerns about the quality of health care services, nurse executives are seeking ways to transform their organizations to improve operational and financial performance while enhancing quality care and patient safety. Nurse leaders are challenged to meet new cost, quality and service imperatives, and change cannot be achieved by traditional approaches, it must occur through innovation. Imagine an organization that can mitigate a $56 million loss in revenue and claim the following successes: Increase admissions by a 8 day and a $5.5 million annualized increase by repurposing existing space. Decrease emergency department holding hours by an average of 174 hours a day, with a labor savings of $502,000 annually. Reduce overall inpatient length of stay by 0.5 day with total compensation running $4.2 million less than the budget for first quarter of 2010. Grow emergency department volume 272 visits greater than budgeted for first quarter of 2010. Complete admission assessments and diagnostics in 90 minutes. This article will address how these outcomes were achieved by transforming care delivery, creating a patient transition center, enhancing outreach referrals, and revising admission processes through collaboration and innovation.
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 | 0.013 | 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 | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".