An environmental scan of quality indicators in critical care
Bibliographic record
Abstract
BACKGROUND: We performed a directed environmental scan to identify and categorize quality indicators unique to critical care that are reported by key stakeholder organizations. METHODS: = 9) to identify key organizations that are focused on quality improvement or critical care, and reviewed their online publications and website content for quality indicators. We identified quality indicators specific to the care of critically ill adult patients and then categorized them according to the Donabedian and the Institute of Medicine frameworks. We also noted the organizations' rationale for selecting these indicators and their reported evidence base. RESULTS: From 28 targeted organizations, we identified 222 quality indicators, 127 of which were unique. Of the 127 indicators, 63 (32.5%) were safety indicators and 61 (31.4%) were effectiveness indicators. The rationale for selecting quality indicators was supported by consensus for 58 (26.1%) of the 222 indicators and by published research evidence for 45 (20.3%); for 119 indicators (53.6%), the rationale was not reported or the reader was referred to other organizations' reports. Of the 127 unique quality indicators, 27 (21.2%) were accompanied by a formal grading of evidence, whereas for 52 (40.9%), no reference to evidence was provided. INTERPRETATION: There are many quality indicators related to critical care that are available in the public domain. However, owing to a paucity of rationale for selection, supporting evidence and results of implementation, it is not clear which indicators should be adopted for use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".