Infrastructure for quality transformation: measurement and reporting in veterans administration intensive care units
Bibliographic record
Abstract
BACKGROUND Veterans Health Administration (VA) intensive care units (ICUs) develop an infrastructure for quality improvement using information technology and recruiting leadership. METHODS Setting Participation by the 183 ICUs in the quality improvement program is required. Infrastructure includes measurement (electronic data extraction, analysis), quarterly web-based reporting and implementation support of evidence-based practices. Leaders prioritise measures based on quality improvement objectives. The electronic extraction is validated manually against the medical record, selecting hospitals whose data elements and measures fall at the extremes (10th, 90th percentile). results are depicted in graphic, narrative and tabular reports benchmarked by type and complexity of ICU. RESULTS The VA admits 103 689±1156 ICU patients/year. Variation in electronic business practices, data location and normal range of some laboratory tests affects data quality. A data management website captures data elements important to ICU performance and not available electronically. A dashboard manages the data overload (quarterly reports ranged 106-299 pages). More than 85% of ICU directors and nurse managers review their reports. Leadership interest is sustained by including ICU targets in executive performance contracts, identification of local improvement opportunities with analytic software, and focused reviews. CONCLUSION Lessons relevant to non-VA institutions include the: (1) need for ongoing data validation, (2) essential involvement of leadership at multiple levels, (3) supplementation of electronic data when key elements are absent, (4) utility of a good but not perfect electronic indicator to move practice while improving data elements and (5) value of a dashboard.
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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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".