Bibliographic record
Abstract
How does a healthcare organization undergo such transformation as described in the lead paper in eight short years? Just imagine being part of an organization that achieved the following transformations: (1) reduction in hospital and long-term-care beds from 92,000 to 53,000 and an increase in outpatient clinics from 200 to 850 (2) a 75% increase in the number of patients treated on an annual basis (from 2.8 million to 4.9 million) with only a 32% cumulative increase in budget (from $19 billion to $25 billion) (3) clinicians who have access to complete medical records for almost all patient visits and all care settings (4) clinicians who willingly enter medication orders 94% of the time (5) patients who are increasingly satisfied with their care, ranking the service consistently higher than the competition (6) improved patient outcomes, achieved at costs 25% less than the competition. Such transformation is impossible to achieve without vision, leadership, talent, teamwork and tools. I will restrict my comments to a discussion of the tools, specifically the VA's clinical information system (VistA, HealtheVet, My HealtheVet. However, it is important to note that the results described in this paper would not be possible without the VA's transformational leadership and dedicated teams of professionals capable of executing the vision.
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 | 0.255 | 0.447 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.046 | 0.054 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".